Frequency adaptive learning circuit with steady-state switching function and method thereof

Abstract

This invention discloses a frequency adaptive learning circuit and method with steady-state switching function, comprising a main circuit, a damping term circuit, a nonlinear term circuit, and a learning rule circuit. The input terminal of the main circuit serves as the input signal terminal of the frequency adaptive learning circuit, receiving a weak input characteristic signal and a high-frequency excitation auxiliary signal. The input terminals of the damping term circuit and the nonlinear term circuit are electrically connected to the output terminal of the main circuit, and their output terminals are also electrically connected to the input terminal of the main circuit. The input terminal of the learning rule circuit receives the weak input characteristic signal and the high-frequency excitation auxiliary signal, and its output terminal is electrically connected to the input terminal of the main circuit. The steady-state switching module in the learning rule circuit switches the frequency adaptive learning circuit between a monostable operating state and a bistable operating state. In the monostable operating state, the weak input characteristic signal is denoised, while in the bistable operating state, the weak input characteristic signal is amplified.

Description

Technical Field

[0001] This invention relates to the field of weak signal processing technology, and in particular to a frequency adaptive learning circuit and method with steady-state switching function. Background Technology

[0002] In fields such as vibration signal detection, condition monitoring, and weak signal processing, target signals often have small amplitudes and are easily submerged by noise due to limitations in sensor sensitivity, environmental interference, and system noise, making signal feature extraction difficult. Stochastic resonance is a physical phenomenon that utilizes the effect of noise on a nonlinear system to enhance weak signals. This theory shows that, under appropriate system parameters and noise intensity, noise not only does not weaken the signal but can also synergistically work with the system's nonlinear characteristics to enhance the system's output response to weak characteristic signals.

[0003] However, the implementation of random resonance typically relies on environmental noise or artificially introduced noise, and its enhancement effect is highly sensitive to noise intensity and statistical characteristics. In practical engineering applications, noise is random and uncontrollable, and system parameters often need repeated adjustments, resulting in poor stability and repeatability, which limits the further application of random resonance methods in complex working conditions and engineering systems. Vibration resonance, on the other hand, introduces high-frequency auxiliary signals with controllable amplitude and frequency into a nonlinear system, enabling the system to generate a resonant response to characteristic signals over a wide frequency range, thereby enhancing weak signals. Compared to random resonance, vibration resonance is easier to control, less affected by noise, and has better controllability and stability, making it more suitable for engineering implementation. However, the occurrence of vibration resonance is also limited by the characteristic frequency; it is difficult to generate vibration resonance for high-frequency signals, and the frequency band in which vibration resonance occurs is low. Therefore, it is necessary to study novel frequency adaptive learning systems that can generate vibration resonance for high-frequency characteristic signals over a wide frequency range, thereby achieving better signal denoising and enhancement effects. Summary of the Invention

[0004] Purpose of the invention: In order to overcome the shortcomings of the prior art, the present invention provides a frequency adaptive learning circuit and method with steady-state switching function, including a main circuit, a damping term circuit, a nonlinear term circuit and a learning rule circuit. The steady-state switching module in the learning rule circuit switches between monostable and bistable operating states to achieve noise reduction or enhancement of weak input characteristic signals.

[0005] Technical Solution: To achieve the above objectives, the present invention provides a frequency adaptive learning circuit with steady-state switching function, comprising a main circuit, a damping term circuit, a nonlinear term circuit, and a learning rule circuit. The input terminal of the main circuit serves as the input signal terminal of the frequency adaptive learning circuit, receiving a weak input characteristic signal and a high-frequency excitation auxiliary signal. The output terminal of the main circuit serves as the output signal terminal of the frequency adaptive learning circuit. The input terminals of the damping term circuit and the nonlinear term circuit are electrically connected to the output terminal of the main circuit, and the output terminals of the damping term circuit and the nonlinear term circuit are electrically connected to the input terminal of the main circuit, forming a closed-loop feedback circuit structure. The input terminal of the learning rule circuit receives a weak input characteristic signal and a high-frequency excitation auxiliary signal, and the output terminal of the learning rule circuit is electrically connected to the input terminal of the main circuit. The learning rule circuit switches the frequency adaptive learning circuit to a monostable or bistable operating state through a steady-state switching module. In the monostable operating state, the frequency adaptive learning circuit performs noise reduction processing on the weak input characteristic signal, while in the bistable operating state, the frequency adaptive learning circuit enhances the weak input characteristic signal.

[0006] Furthermore, the main circuit includes a first adder module and an integrator module; the integrator module includes a first integrator module, a K1 first gain module, and a second integrator module; the input terminal of the first adder module serves as the input terminal of the main circuit, the output terminal of the first adder module is electrically connected to the input terminal of the first integrator module, the output terminal of the first integrator module is electrically connected to the non-inverting input terminal of the K1 first gain module, the non-inverting output terminal of the K1 first gain module is electrically connected to the input terminal of the second integrator module, and the output terminal of the second integrator module serves as the output terminal of the main circuit.

[0007] Furthermore, the first adding module includes resistor R4 and operational amplifier U1; the first integrating module includes variable resistor R5, resistor R16, variable capacitor C1, and operational amplifier U2; the second integrating module includes variable resistor R6, resistor R17, variable capacitor C2, and operational amplifier U3; the inverting input terminal of operational amplifier U1 serves as the input terminal of the first adding module, and the inverting input terminal of operational amplifier U1 is electrically connected to the output terminal of operational amplifier U1 through resistor R4; the output terminal of operational amplifier U1 is electrically connected to one end of variable resistor R5, and the other end of variable resistor R5 is electrically connected to operational amplifier U2. The inverting input terminal of the U2 operational amplifier is electrically connected to the output terminal of the U2 operational amplifier through a parallel resistor R16 and a variable capacitor C1. The output terminal of the U2 operational amplifier is electrically connected to the non-inverting input terminal of the K1 first gain module. The non-inverting output terminal of the K1 first gain module is electrically connected to one end of the R6 variable resistor. The other end of the R6 variable resistor is electrically connected to the inverting input terminal of the U3 operational amplifier. The inverting input terminal of the U3 operational amplifier is electrically connected to the output terminal of the U3 operational amplifier through a parallel resistor R17 and a variable capacitor C2. The output terminal of the U3 operational amplifier serves as the output terminal of the second integration module.

[0008] Furthermore, the damping phase circuit includes an input resistor R7, a resistor R9, an operational amplifier U4, and a resistor R10; one end of the input resistor R7 serves as the input terminal of the damping phase circuit and is electrically connected to the non-inverting output terminal of the first gain module K1; the other end of the input resistor R7 is electrically connected to the inverting input terminal of the operational amplifier U4, and the inverting input terminal of the operational amplifier U4 is electrically connected to the output terminal of the operational amplifier U4 through the resistor R9; the output terminal of the operational amplifier U4 is electrically connected to one end of the resistor R10, and the other end of the resistor R10 serves as the output terminal of the damping phase circuit.

[0009] Furthermore, the nonlinear term circuit includes a first multiplier A1, a second multiplier A2, an input resistor R8, a resistor R9, an operational amplifier U4, and a resistor R10. The Y-terminal of the first multiplier A1, the X-terminal and the Y-terminal of the second multiplier A2 are all electrically connected to the output terminal of the operational amplifier U3, and the output terminal of the second multiplier A2 is electrically connected to the X-terminal of the first multiplier A1. The output terminal of the first multiplier A1 is electrically connected to one end of the input resistor R8, and the other end of the input resistor R8 is electrically connected to the inverting input terminal of the operational amplifier U4. The inverting input terminal of the operational amplifier U4 is electrically connected to the output terminal of the operational amplifier U4 through the resistor R9. The output terminal of the operational amplifier U4 is electrically connected to one end of the resistor R10, and the other end of the resistor R10 serves as the output terminal of the nonlinear term circuit.

[0010] Furthermore, the learning rule circuit includes a second addition module, a third integration module, an A3 third multiplier, an A4 fourth multiplier, and a steady-state switching module; the input terminal of the second addition module serves as the input terminal of the learning rule circuit, the output terminal of the second addition module is electrically connected to the input terminal of the third integration module, the output terminal of the third integration module is electrically connected to the X and Y terminals of the A4 fourth multiplier, the output terminal of the A4 fourth multiplier is electrically connected to the Y terminal of the A3 third multiplier, and the X terminal of the A3 third multiplier is electrically connected to the output terminal of the main circuit; the output terminal of the A3 third multiplier is electrically connected to the input terminal of the steady-state switching module, and the output terminal of the steady-state switching module is electrically connected to the input terminal of the main circuit.

[0011] Furthermore, the second adding module includes a variable resistor R11, a variable resistor R12, a resistor R13, and an operational amplifier U5; the third integrating module includes a resistor R14, a resistor R15, a capacitor C3, and an operational amplifier U6; one end of the variable resistor R11 and one end of the variable resistor R12 serve as input terminals of the second adding module, respectively inputting a weak input characteristic signal and a high-frequency excitation auxiliary signal; the other ends of the variable resistor R11 and the other ends of the variable resistor R12 are electrically connected to the inverting input terminal of the operational amplifier U5, and the inverting input terminal of the operational amplifier U5 is electrically connected to the output terminal of the operational amplifier U5 through the resistor R13; the output terminal of the operational amplifier U5 is electrically connected to one end of the resistor R14, and the other end of the resistor R14 is electrically connected to the inverting input terminal of the operational amplifier U6, and the inverting input terminal of the operational amplifier U6 is electrically connected to the output terminal of the operational amplifier U6 through the parallel resistor R15 and the capacitor C3, and the output terminal of the operational amplifier U6 serves as the output terminal of the third integrating module.

[0012] Furthermore, the steady-state switching module includes a K2 second gain module, an S1 switch, and an R3 resistor; the non-inverting input terminal of the K2 second gain module serves as the input terminal of the steady-state switching module, the non-inverting input terminal of the K2 second gain module is electrically connected to the non-inverting output terminal of the K2 second gain module through the S1 switch, the non-inverting output terminal of the K2 second gain module is electrically connected to one end of the R3 resistor, and the other end of the R3 resistor serves as the output terminal of the steady-state switching module; when the S1 switch is open, the frequency adaptive learning circuit switches to monostable operating state; when the S1 switch is closed, the frequency adaptive learning circuit switches to bistable operating state.

[0013] Furthermore, the system equations of the frequency adaptive learning circuit are as follows:

[0014]

[0015] In the formula, ζ, b, ω and β represent system parameters, and k ωLet f1(t) represent the learning rate, f2(t) represent the weak input characteristic signal, and f2(t) represent the high-frequency excitation auxiliary signal.

[0016] Furthermore, a method for operating a frequency adaptive learning circuit with steady-state switching capability includes the following steps:

[0017] Step 1: Based on the processing requirements of weak input feature signals, the frequency adaptive learning circuit is switched to monostable or bistable operating state through the steady-state switching module.

[0018] Step 2: When the frequency adaptive learning circuit switches to monostable operation, perform power spectrum estimation analysis on the weak input feature signal to obtain the power spectrum analysis results containing the weak input feature signal; based on the power spectrum analysis results and combined with the system equation of the frequency adaptive learning circuit, select a high-frequency excitation auxiliary signal that matches the frequency range of the weak input feature signal.

[0019] Step 3: When the frequency adaptive learning circuit switches to the bistable operating state, perform power spectrum estimation analysis on the weak input feature signal to obtain the power spectrum analysis results containing the weak input feature signal; based on the power spectrum analysis results and combined with the system equation of the frequency adaptive learning circuit, select a high-frequency excitation auxiliary signal that matches the frequency of the weak input feature signal.

[0020] Step 4: Adjust the parameters of all rheostats in the learning rule module so that the frequency adaptive learning process in the frequency adaptive learning circuit gradually converges until the non-target frequency components in the output signal are effectively suppressed and the output signal presents a frequency-stable approximate simple harmonic waveform.

[0021] Step 5: Under the condition that the steady-state working state, the parameters of the high-frequency excitation auxiliary signal and the parameters in the learning rule module remain unchanged, adjust the parameters of all rheostats and variable capacitors in the first integration module, the second integration module and the third integration module until a pure output signal with high spectral purity and reduced noise components is obtained.

[0022] Beneficial Effects: This invention provides a frequency adaptive learning circuit and method with steady-state switching functionality. By introducing a steady-state switching module into the frequency adaptive learning circuit, the circuit can flexibly switch between monostable and bistable operating states. This enables noise reduction and feature enhancement of weak characteristic signals within the same hardware architecture, avoiding the need for multiple circuits or complex switching structures in traditional solutions for different processing requirements, thus improving system integration and flexibility. The circuit is implemented using analog circuits based on operational amplifiers, resistors, capacitors, and multipliers, resulting in a relatively simple structure, good real-time performance, and ease of engineering implementation, making it suitable for widespread application in embedded systems and real-time signal processing scenarios. Since the high-frequency excitation auxiliary signal parameters and key circuit parameters can be flexibly adjusted, the proposed circuit structure can not only effectively process specific target frequencies but also meet the processing needs of weak characteristic signals over a wide frequency range, exhibiting good versatility. During signal processing, power spectrum analysis is combined to obtain the frequency prior information of the target signal, and a matching high-frequency excitation auxiliary signal is selected accordingly. This gives the vibration resonance process clear frequency directivity and adaptive adjustment capability, reducing the uncertainty caused by blindly setting parameters and improving the system's adaptability to different frequency components. Attached Figure Description

[0023] Figure 1 The circuit diagram is for a frequency adaptive learning circuit with steady-state switching function;

[0024] Figure 2 The image shows the weak input characteristic signal of the simulated signal under monostable operating conditions and the effect of signal processing.

[0025] Figure 3 The image shows the weak input characteristic signal of the simulated signal under bistable operating conditions and the effect of signal processing.

[0026] Figure 4 The diagram shows the signal processing effect of the integration module under three different resistors and capacitors.

[0027] Figure 5 The time-domain plot and spectrum of the acquired characteristic signal and the output signal after processing by the monostable operating system are shown.

[0028] Figure 6 The time-domain plot and spectrum plot of the acquired characteristic signal and the output signal after processing by the bistable operating system are shown. Detailed Implementation

[0029] The invention will now be further described with reference to the accompanying drawings.

[0030] like Figure 1As shown, a frequency adaptive learning circuit with steady-state switching function includes a main circuit, a damping term circuit, a nonlinear term circuit, and a learning rule circuit. The input terminal of the main circuit serves as the input signal terminal of the frequency adaptive learning circuit, receiving a weak input characteristic signal and a high-frequency excitation auxiliary signal. The output terminal of the main circuit serves as the output signal terminal of the frequency adaptive learning circuit. The input terminals of the damping term circuit and the nonlinear term circuit are electrically connected to the output terminal of the main circuit, and the output terminals of the damping term circuit and the nonlinear term circuit are electrically connected to the input terminal of the main circuit, forming a closed-loop feedback circuit structure. The input terminal of the learning rule circuit receives a weak input characteristic signal and a high-frequency excitation auxiliary signal, and the output terminal of the learning rule circuit is electrically connected to the input terminal of the main circuit. The learning rule circuit switches the frequency adaptive learning circuit to a monostable or bistable operating state through a steady-state switching module. In the monostable operating state, the resonant output of the system in the frequency adaptive learning circuit performs noise reduction processing on the weak input characteristic signal, while in the bistable operating state, the resonant output of the system in the frequency adaptive learning circuit enhances the weak input characteristic signal.

[0031] The main circuit includes a first adder module and an integrator module; the integrator module includes a first integrator module, a K1 first gain module, and a second integrator module; the input terminal of the first adder module serves as the input terminal of the main circuit, the output terminal of the first adder module is electrically connected to the input terminal of the first integrator module, the output terminal of the first integrator module is electrically connected to the non-inverting input terminal of the K1 first gain module, the non-inverting output terminal of the K1 first gain module is electrically connected to the input terminal of the second integrator module, and the output terminal of the second integrator module serves as the output terminal of the main circuit, outputting a clean output signal.

[0032] The first adding module includes resistor R4 and operational amplifier U1; the first integrating module includes rheostat R5, resistor R16, variable capacitor C1, and operational amplifier U2; the second integrating module includes rheostat R6, resistor R17, variable capacitor C2, and operational amplifier U3; the inverting input terminal of operational amplifier U1 serves as the input terminal of the first adding module, and the inverting input terminal of operational amplifier U1 is electrically connected to the output terminal of operational amplifier U1 through resistor R4; the non-inverting input terminal of operational amplifier U1 is grounded; the output terminal of operational amplifier U1 is electrically connected to one end of rheostat R5, and the other end of rheostat R5 is electrically connected to the inverting input terminal of operational amplifier U2; the inverting input terminal of operational amplifier U2 is connected to... The parallel resistor R16 and variable capacitor C1 are electrically connected to the output terminal of operational amplifier U2, and the non-inverting input terminal of operational amplifier U2 is grounded. The output terminal of operational amplifier U2 is electrically connected to the non-inverting input terminal of the first gain module K1, and the non-inverting output terminal of the first gain module K1 is electrically connected to one end of the variable resistor R6. The inverting input terminal and the inverting output terminal of the first gain module K1 are both grounded. The other end of the variable resistor R6 is electrically connected to the inverting input terminal of operational amplifier U3, and the inverting input terminal of operational amplifier U3 is electrically connected to the output terminal of operational amplifier U3 through the parallel resistor R17 and variable capacitor C2. The non-inverting input terminal of operational amplifier U3 is grounded, and the output terminal of operational amplifier U3 serves as the output terminal of the second integration module.

[0033] The damping phase circuit includes an input resistor R7, a resistor R9, an operational amplifier U4, and a resistor R10. One end of the input resistor R7 serves as the input terminal of the damping phase circuit and is electrically connected to the non-inverting output terminal of the first gain module K1. The other end of the input resistor R7 is electrically connected to the inverting input terminal of the operational amplifier U4. The inverting input terminal of the operational amplifier U4 is electrically connected to the output terminal of the operational amplifier U4 through the resistor R9. The non-inverting input terminal of the operational amplifier U4 is grounded. The output terminal of the operational amplifier U4 is electrically connected to one end of the resistor R10, and the other end of the resistor R10 serves as the output terminal of the damping phase circuit.

[0034] The nonlinear term circuit includes a first multiplier A1, a second multiplier A2, an input resistor R8, a resistor R9, an operational amplifier U4, and a resistor R10. The Y-terminal of the first multiplier A1, and the X and Y-terminals of the second multiplier A2 are all electrically connected to the output terminal of the operational amplifier U3. The output terminal of the second multiplier A2 is electrically connected to the X-terminal of the first multiplier A1. The output terminal of the first multiplier A1 is electrically connected to one end of the input resistor R8. The other end of the input resistor R8 is electrically connected to the inverting input terminal of the operational amplifier U4. The inverting input terminal of the operational amplifier U4 is electrically connected to the output terminal of the operational amplifier U4 through the resistor R9. The non-inverting input terminal of the operational amplifier U4 is grounded. The output terminal of the operational amplifier U4 is electrically connected to one end of the resistor R10. The other end of the resistor R10 serves as the output terminal of the nonlinear term circuit.

[0035] The damping phase circuit and the nonlinear phase circuit share a common proportional module, which is a proportional module consisting of resistor R9, operational amplifier U4, and resistor R10. However, the input resistors used in the damping phase circuit and the nonlinear phase circuit are different. In the damping phase circuit, the proportional module uses resistor R7 as the input resistor; in the nonlinear phase circuit, the proportional module uses resistor R8 as the input resistor.

[0036] The learning rule circuit includes a second addition module, a third integration module, an A3 third multiplier, an A4 fourth multiplier, and a steady-state switching module. The input terminal of the second addition module serves as the input terminal of the learning rule circuit. The output terminal of the second addition module is electrically connected to the input terminal of the third integration module. The output terminal of the third integration module is electrically connected to the X and Y terminals of the A4 fourth multiplier. The output terminal of the A4 fourth multiplier is electrically connected to the Y terminal of the A3 third multiplier. The X terminal of the A3 third multiplier is electrically connected to the output terminal of the main circuit. The output terminal of the A3 third multiplier is electrically connected to the input terminal of the steady-state switching module, and the output terminal of the steady-state switching module is electrically connected to the input terminal of the main circuit.

[0037] The second adding module includes a variable resistor R11, a variable resistor R12, a resistor R13, and an operational amplifier U5; the third integrating module includes a resistor R14, a resistor R15, a capacitor C3, and an operational amplifier U6; one end of the variable resistor R11 and one end of the variable resistor R12 serve as input terminals of the second adding module, respectively inputting a weak input characteristic signal and a high-frequency excitation auxiliary signal; the other ends of the variable resistors R11 and R12 are electrically connected to the inverting input terminal of the operational amplifier U5. The inverting input terminal is electrically connected to the output terminal of the U5 operational amplifier through resistor R13, and the non-inverting input terminal of the U5 operational amplifier is grounded; the output terminal of the U5 operational amplifier is electrically connected to one end of resistor R14, and the other end of resistor R14 is electrically connected to the inverting input terminal of the U6 operational amplifier. The inverting input terminal of the U6 operational amplifier is electrically connected to the output terminal of the U6 operational amplifier through resistor R15 and capacitor C3 in parallel, and the non-inverting input terminal of the U6 operational amplifier is grounded. The output terminal of the U6 operational amplifier serves as the output terminal of the third integration module.

[0038] The steady-state switching module includes a K2 second gain module, an S1 switch, and an R3 resistor. The non-inverting input of the K2 second gain module serves as the input of the steady-state switching module. The non-inverting input of the K2 second gain module is electrically connected to the non-inverting output of the K2 second gain module via the S1 switch. The non-inverting output of the K2 second gain module is electrically connected to one end of the R3 resistor. Both the inverting input and output of the K2 first gain module are grounded. The other end of the R3 resistor serves as the output of the steady-state switching module. Both the K1 first gain module and the K2 second gain module are voltage gain modules, and any module capable of achieving voltage gain can be used, such as the AD8367. When the S1 switch is open, the frequency adaptive learning circuit switches to monostable operation; when the S1 switch is closed, the frequency adaptive learning circuit switches to bistable operation.

[0039] The system equations for the frequency adaptive learning circuit are shown below:

[0040]

[0041] In the formula, ζ, b, ω and β represent system parameters, and k ω Let f1(t) represent the weak input characteristic signal carrying feature information, and f2(t) represent the high-frequency excitation auxiliary signal, which plays a role in assisting in adjusting the system output; when ω 2 When the sign before x is "-", ω 2 The coefficient of x is -1, the system is a monostable system, when ω 2 When the sign before x is "+", ω 2 The coefficient of x is 1, and the system is a bistable operating system.

[0042] Based on the system equations and circuit structure of the fundamental frequency adaptive learning circuit, an analog circuit is built in Multisim, and the circuit differential equation of the frequency adaptive learning circuit is defined as follows:

[0043]

[0044] In the formula, This is represented as a weak input characteristic signal. R1 represents the high-frequency excitation auxiliary signal; Ω1 represents the angular frequency of the weak input characteristic signal; Ω2 represents the angular frequency of the high-frequency excitation auxiliary signal; ω represents the system parameters; where R1, R2, R3, R4, R6, R7, R8, R9, R 10 R 11 R 12 R 13 C1, C2, and C3 represent the resistance values ​​of their respective resistors; C1, C2, and C3 represent the capacitance values ​​of their respective capacitors; and K1 and K2 represent the gain values ​​of their respective gain modules.

[0045] A method for operating a frequency adaptive learning circuit with steady-state switching capability includes the following steps:

[0046] Step 1: Based on the processing requirements of weak input feature signals, the frequency adaptive learning circuit is switched to either monostable or bistable operating state through the steady-state switching module; this is to achieve noise reduction or feature information enhancement processing of weak input feature signals respectively.

[0047] Step 2: When the frequency adaptive learning circuit switches to monostable operation, power spectrum estimation analysis is performed on the weak input feature signal to obtain the power spectrum analysis results containing the energy distribution characteristics and frequency prior range of the weak input feature signal. Based on the power spectrum analysis results and combined with the system equation of the frequency adaptive learning circuit, a high-frequency excitation auxiliary signal matching the frequency range of the weak input feature signal is selected, and the high-frequency excitation auxiliary signal and the weak input feature signal are introduced into the frequency adaptive learning circuit for noise reduction processing.

[0048] Step 3: When the frequency adaptive learning circuit switches to bistable operation, power spectrum estimation analysis is performed on the weak input feature signal to obtain the power spectrum analysis results containing the frequency prior information of the weak input feature signal; based on the power spectrum analysis results and combined with the system equation of the frequency adaptive learning circuit, a high-frequency excitation auxiliary signal that matches the frequency of the weak input feature signal is selected, and the high-frequency excitation auxiliary signal and the weak input feature signal are introduced into the frequency adaptive learning circuit for feature information enhancement processing;

[0049] The process of performing power spectrum estimation analysis on weak input feature signals to obtain power spectrum analysis results containing weak input feature signals includes the following steps:

[0050] Step S1-1: Discretely sample the weak input feature signal f1(t) to obtain the discrete signal. Where N is the number of sampling points, and the corresponding sampling frequency is f. s The discrete sampled signal is preprocessed to obtain the preprocessed signal x'(n); the DC component is removed from the discrete signal, and the calculation process is as follows:

[0051]

[0052] To eliminate the influence of DC bias on the power spectrum estimation results, the preprocessed signal x'(n) is divided into L segments of length M. The i-th segment is represented as: Where L = [(NM) / (M / 2)] + 1;

[0053] Step S1-2: To reduce spectral leakage, each signal segment is multiplied by the Hamming window function ω(n) to obtain the windowed signal segment x. ωi (n), and calculate the window function normalization factor U; the calculation process is shown below:

[0054]

[0055]

[0056]

[0057] Steps S1-3: For each segment of the windowed signal x ωi (n) Perform a Fast Fourier Transform (FFT) to obtain each segment of the signal after the FFT; the calculation process is shown below:

[0058]

[0059] In the formula, k = 0, 1, ..., M-1; the corresponding frequency is f. k =(kf s ) / M;

[0060] Steps S1-4: Calculate the power spectral density estimate P for each segment of the signal after the Fast Fourier Transform. i (k), power spectrum estimate P i The calculation process for (k) is shown below:

[0061]

[0062] The periodogram of segment L is then averaged to obtain the power spectrum estimate of segment L, resulting in the final Welch power spectrum estimate P. xx (k) is used to obtain a smooth power spectral density estimate; the calculation process is shown below:

[0063]

[0064] Step S1-5: Calculate the main peak frequency f based on the power spectral density estimation results. p and corresponding angular frequency ω p The minimum frequency range where the cumulative power reaches 90% or more is determined, which serves as the frequency prior range information for the weak input characteristic signal; the calculation process is shown below:

[0065] ,

[0066] .

[0067] Based on the power spectrum analysis results and the system equations of the frequency adaptive learning circuit, a high-frequency excitation auxiliary signal matching the frequency of the weak input characteristic signal is selected. To achieve the vibration resonance effect, the frequency of the high-frequency excitation auxiliary signal should satisfy the following condition: Ω2≫ω p ω p Let Ω be the angular frequency of the weak input characteristic signal obtained through power spectrum estimation; preferably satisfying: Ω2 ≫ 5~10ω p To ensure scale separation between high-frequency components and the system's full-variable response, a fast-slow variable separation method is then used to perform high-frequency separation on the system, yielding an equivalent slow variable equation. The calculation process is shown below:

[0068] ,

[0069] ,

[0070] In the formula, ω 2 eff For equivalent stiffness, ω and β are system parameters; Ω2 and A are the angular frequency and amplitude of the high-frequency auxiliary excitation signal, respectively; to ensure the system produces the best response to weak input characteristic signals, the resonance condition should be satisfied: ω eff ≈ω p From this, we can deduce the formula for selecting the amplitude of the high-frequency excitation auxiliary signal and calculate the angular frequency amplitude of the high-frequency auxiliary excitation signal. The calculation process is shown below:

[0071]

[0072] Therefore, the amplitude and frequency of the high-frequency excitation auxiliary signal can be determined, and it can be used together with the input signal in the frequency adaptive learning circuit.

[0073] Step 4: Adjust the parameters of all rheostats in the learning rule module to gradually converge the frequency adaptive learning process in the frequency adaptive learning circuit until the non-target frequency components in the output signal are effectively suppressed and the output signal presents a frequency-stable approximate simple harmonic waveform. Adjusting the parameters of all rheostats in the learning rule module means adjusting all rheostats one by one, which can be done by gradually adjusting the rheostats from small to large until the output signal presents a frequency-stable approximate simple harmonic waveform.

[0074] Step 5: While maintaining the steady-state operation, high-frequency excitation auxiliary signal parameters, and parameters in the learning rule module, adjust the parameters of all rheostats and variable capacitors in the first, second, and third integration modules to optimize the system's time constant and dynamic response characteristics until a clean output signal with high spectral purity and reduced noise components is obtained, resulting in good signal processing performance. Adjusting the parameters of all rheostats and variable capacitors in the first, second, and third integration modules involves adjusting each rheostat and variable capacitor individually within the integration modules. This can be done by gradually increasing the values ​​of the rheostats and variable capacitors until a clean output signal with high spectral purity and reduced noise components is obtained.

[0075] Example

[0076] like Figure 2 The image shows the weak input characteristic signal of the simulated signal and the effect of signal processing under monostable operating conditions; as shown... Figure 3 The image shows the weak input characteristics of the simulated signal and the effect of signal processing under bistable operating conditions; as shown... Figure 4 The image shows the signal processing effect of the integration module under three different resistor and capacitor settings; as shown... Figure 5 The figure shows the time-domain and spectrum diagrams of the characteristic signals acquired based on passive RFID tags and the output signals after resonance processing by a monostable learning system; as shown. Figure 6 The figure shows the time-domain and spectrum diagrams of the characteristic signals acquired based on passive RFID tags and the output signal after resonance processing by a bistable operating state learning system. Figure 2 , Figure 3 , Figure 4 , Figure 5 and Figure 6 It can be seen that the frequency adaptive learning circuit with steady-state switching function of the present invention can perform good and effective noise reduction on weak input feature signals in monostable operation and significantly enhance the feature information of weak input feature signals in bistable operation.

[0077] by Figure 3For example, in the specific implementation process, the input simulation signal is constructed in the MATLAB simulation environment. The characteristic frequency is 5 rad / s, the sampling frequency is 200 Hz, and noise is added to reduce the signal-to-noise ratio to -25 dB. First, for the input signal under the aforementioned strong noise background, a power spectrum estimation method is used to perform frequency domain analysis to obtain the a priori range of the characteristic frequencies that the target input signal may exist. Combined with the established system equations, a high-frequency excitation auxiliary signal matching the frequency range is determined. Then, switch S1 is turned off, and the frequency adaptive learning circuit switches to monostable operation, enabling the circuit to operate in noise reduction mode. The noisy input signal and the high-frequency excitation auxiliary signal are simultaneously input into the circuit. By adjusting the R11 and R12 rheostats in the learning rule module, the system parameters are adjusted. The settings and optimizations are performed until non-target frequency components in the output signal are suppressed to the expected level, resulting in an output signal that presents a relatively stable, approximately simple harmonic waveform. Based on the above parameter configurations, the rheostats and variable capacitors in all integration modules are further adjusted to optimize the system's time constant and dynamic response characteristics. Combined with... Figure 4 The results show that different combinations of resistor and capacitor parameters have a significant impact on the output signal waveform: Figure 4 (a) Under the parameter conditions shown, when the resistance is 100kΩ and the capacitance is 10μF, the amplitude of the output signal exhibits obvious fluctuation characteristics over time; Figure 4 (b) Under the parameter conditions shown, when the resistance is 200kΩ and the capacitance is 5μF, the target frequency component accounts for a relatively high proportion in the output signal, and the overall waveform is relatively stable. Figure 4 (c) Under the parameter conditions shown, when the resistor is 500kΩ and the capacitor is 2μF, the proportion of high-frequency components in the processed output signal increases significantly, affecting the purity of the signal. After comprehensively comparing the output effects under different parameter combinations, the parameter configuration of 200kΩ resistor and 5μF capacitor was finally selected as the optimal setting for the integration module, thus obtaining the following result: Figure 3 The output signal shown is a clean output signal with high spectral purity and reduced noise components.

[0078] The above description is merely a preferred embodiment of the present invention. Those skilled in the art can make several modifications and optimizations based on the above disclosure without departing from the basic principles described above. These modifications and optimizations should be considered within the scope of protection as understood by the present invention.

Claims

1. A frequency adaptive learning circuit with steady-state switching function, characterized in that: The circuit includes a main circuit, a damping term circuit, a nonlinear term circuit, and a learning rule circuit. The input terminal of the main circuit serves as the input signal terminal of the frequency adaptive learning circuit, receiving a weak input characteristic signal and a high-frequency excitation auxiliary signal. The output terminal of the main circuit serves as the output signal terminal of the frequency adaptive learning circuit. The input terminals of the damping term circuit and the nonlinear term circuit are electrically connected to the output terminal of the main circuit, and their output terminals are also electrically connected to the input terminal of the main circuit, forming a closed-loop feedback circuit structure. The input terminal of the learning rule circuit receives a weak input characteristic signal and a high-frequency excitation auxiliary signal, and its output terminal is electrically connected to the input terminal of the main circuit. The learning rule circuit uses a steady-state switching module to switch the frequency adaptive learning circuit between monostable and bistable operating states. In monostable operating state, the frequency adaptive learning circuit performs noise reduction processing on the weak input characteristic signal; in bistable operating state, the frequency adaptive learning circuit performs enhancement processing on the weak input characteristic signal.

2. The frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: The main circuit includes a first adder module and an integrator module; the integrator module includes a first integrator module, a K1 first gain module, and a second integrator module; the input terminal of the first adder module serves as the input terminal of the main circuit, the output terminal of the first adder module is electrically connected to the input terminal of the first integrator module, the output terminal of the first integrator module is electrically connected to the non-inverting input terminal of the K1 first gain module, the non-inverting output terminal of the K1 first gain module is electrically connected to the input terminal of the second integrator module, and the output terminal of the second integrator module serves as the output terminal of the main circuit.

3. The frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: The first adding module includes resistor R4 and operational amplifier U1; the first integrating module includes rheostat R5, resistor R16, variable capacitor C1, and operational amplifier U2; the second integrating module includes rheostat R6, resistor R17, variable capacitor C2, and operational amplifier U3; the inverting input of operational amplifier U1 serves as the input of the first adding module, and the inverting input of operational amplifier U1 is electrically connected to the output of operational amplifier U1 through resistor R4; the output of operational amplifier U1 is electrically connected to one end of rheostat R5, and the other end of rheostat R5 is electrically connected to the inverting input of operational amplifier U2. The inverting input of operational amplifier U2 is electrically connected to its output via a parallel resistor R16 and a variable capacitor C1. The output of operational amplifier U2 is electrically connected to the non-inverting input of the first gain module K1. The non-inverting output of the first gain module K1 is electrically connected to one end of a variable resistor R6. The other end of the variable resistor R6 is electrically connected to the inverting input of operational amplifier U3. The inverting input of operational amplifier U3 is electrically connected to its output via a parallel resistor R17 and a variable capacitor C2. The output of operational amplifier U3 serves as the output of the second integration module.

4. The frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: The damping phase circuit includes an input resistor R7, a resistor R9, an operational amplifier U4, and a resistor R10. One end of the input resistor R7 serves as the input terminal of the damping phase circuit and is electrically connected to the non-inverting output terminal of the first gain module K1. The other end of the input resistor R7 is electrically connected to the inverting input terminal of the operational amplifier U4, and the inverting input terminal of the operational amplifier U4 is electrically connected to the output terminal of the operational amplifier U4 through the resistor R9. The output terminal of the operational amplifier U4 is electrically connected to one end of the resistor R10, and the other end of the resistor R10 serves as the output terminal of the damping phase circuit.

5. A frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: The nonlinear term circuit includes a first multiplier A1, a second multiplier A2, an input resistor R8, a resistor R9, an operational amplifier U4, and a resistor R10. The Y-terminal of the first multiplier A1, and the X and Y-terminals of the second multiplier A2 are all electrically connected to the output terminal of the operational amplifier U3. The output terminal of the second multiplier A2 is electrically connected to the X-terminal of the first multiplier A1. The output terminal of the first multiplier A1 is electrically connected to one end of the input resistor R8. The other end of the input resistor R8 is electrically connected to the inverting input terminal of the operational amplifier U4. The inverting input terminal of the operational amplifier U4 is electrically connected to the output terminal of the operational amplifier U4 through the resistor R9. The output terminal of the operational amplifier U4 is electrically connected to one end of the resistor R10. The other end of the resistor R10 serves as the output terminal of the nonlinear term circuit.

6. The frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: The learning rule circuit includes a second addition module, a third integration module, an A3 third multiplier, an A4 fourth multiplier, and a steady-state switching module. The input terminal of the second addition module serves as the input terminal of the learning rule circuit. The output terminal of the second addition module is electrically connected to the input terminal of the third integration module. The output terminal of the third integration module is electrically connected to the X and Y terminals of the A4 fourth multiplier. The output terminal of the A4 fourth multiplier is electrically connected to the Y terminal of the A3 third multiplier. The X terminal of the A3 third multiplier is electrically connected to the output terminal of the main circuit. The output terminal of the A3 third multiplier is electrically connected to the input terminal of the steady-state switching module, and the output terminal of the steady-state switching module is electrically connected to the input terminal of the main circuit.

7. A frequency adaptive learning circuit with steady-state switching function according to claim 6, characterized in that: The second adding module includes a variable resistor R11, a variable resistor R12, a resistor R13, and an operational amplifier U5; the third integrating module includes a resistor R14, a resistor R15, a capacitor C3, and an operational amplifier U6; one end of the variable resistor R11 and one end of the variable resistor R12 serve as the input terminals of the second adding module, respectively inputting a weak input characteristic signal and a high-frequency excitation auxiliary signal; the other ends of the variable resistor R11 and the other ends of the variable resistor R12 are electrically connected to the inverting input terminal of the operational amplifier U5, and the inverting input terminal of the operational amplifier U5 is electrically connected to the output terminal of the operational amplifier U5 through the resistor R13; the output terminal of the operational amplifier U5 is electrically connected to one end of the resistor R14, and the other end of the resistor R14 is electrically connected to the inverting input terminal of the operational amplifier U6, and the inverting input terminal of the operational amplifier U6 is electrically connected to the output terminal of the operational amplifier U6 through the parallel resistor R15 and the capacitor C3, and the output terminal of the operational amplifier U6 serves as the output terminal of the third integrating module.

8. A frequency adaptive learning circuit with steady-state switching function according to claim 6, characterized in that: The steady-state switching module includes a K2 second gain module, an S1 switch, and an R3 resistor. The non-inverting input terminal of the K2 second gain module serves as the input terminal of the steady-state switching module. The non-inverting input terminal of the K2 second gain module is electrically connected to the non-inverting output terminal of the K2 second gain module through the S1 switch. The non-inverting output terminal of the K2 second gain module is electrically connected to one end of the R3 resistor, and the other end of the R3 resistor serves as the output terminal of the steady-state switching module. When switch S1 is open, the frequency adaptive learning circuit switches to monostable operation; when switch S1 is closed, the frequency adaptive learning circuit switches to bistable operation.

9. A frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: The system equations for the frequency adaptive learning circuit are as follows: In the formula, ζ, b, ω and β represent system parameters, and k ω Let f1(t) represent the learning rate, f2(t) represent the weak input characteristic signal, and f2(t) represent the high-frequency excitation auxiliary signal.

10. The operating method of a frequency adaptive learning circuit with steady-state switching function according to claim 1, characterized in that: Includes the following steps: Step 1: Based on the processing requirements of weak input feature signals, the frequency adaptive learning circuit is switched to monostable or bistable operating state through the steady-state switching module. Step 2: When the frequency adaptive learning circuit switches to monostable operation, perform power spectrum estimation analysis on the weak input feature signal to obtain the power spectrum analysis results containing the weak input feature signal; based on the power spectrum analysis results and combined with the system equation of the frequency adaptive learning circuit, select a high-frequency excitation auxiliary signal that matches the frequency range of the weak input feature signal. Step 3: When the frequency adaptive learning circuit switches to the bistable operating state, perform power spectrum estimation analysis on the weak input feature signal to obtain the power spectrum analysis results containing the weak input feature signal; based on the power spectrum analysis results and combined with the system equation of the frequency adaptive learning circuit, select a high-frequency excitation auxiliary signal that matches the frequency of the weak input feature signal. Step 4: Adjust the parameters of all rheostats in the learning rule module so that the frequency adaptive learning process in the frequency adaptive learning circuit gradually converges until the non-target frequency components in the output signal are effectively suppressed and the output signal presents a frequency-stable approximate simple harmonic waveform. Step 5: Under the condition that the steady-state working state, the parameters of the high-frequency excitation auxiliary signal and the parameters in the learning rule module remain unchanged, adjust the parameters of all rheostats and variable capacitors in the first integration module, the second integration module and the third integration module until a pure output signal with high spectral purity and reduced noise components is obtained.

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