33 results about "Nonlinear system identification" patented technology

A device for measuring a mechanical property of a tissue includes a probe configured to perturb the tissue with movement relative to a surface of the tissue, an actuator coupled to the probe to move the probe, a detector configured to measure a response of the tissue to the perturbation, and a controller coupled to the actuator and the detector. The controller drives the actuator using a stochastic sequence and determines the mechanical property of the tissue using the measured response received from the detector. The probe can be coupled to the tissue surface. The device can include a reference surface configured to contact the tissue surface. The probe may include a set of interchangeable heads, the set including a head for lateral movement of the probe and a head for perpendicular movement of the probe. The perturbation can include extension of the tissue with the probe or sliding the probe across the tissue surface and may also include indentation of the tissue with the probe. In some embodiments, the actuator includes a Lorentz force linear actuator. The mechanical property may be determined using non-linear stochastic system identification. The mechanical property may be indicative of, for example, tissue compliance and tissue elasticity. The device can further include a handle for manual application of the probe to the surface of the tissue and may include an accelerometer detecting an orientation of the probe. The device can be used to test skin tissue of an animal, plant tissue, such as fruit and vegetables, or any other biological tissue.

A method of detecting whether a receiver coil is near a transmit coil in a wireless power transfer system (WPTS), the method involving: applying a pseudo-random signal to the transmit coil; while the pseudo-random signal is being applied to the transmit coil, recording one or more signals produced within the WPTS in response to the applied pseudo-random signal; by using the one or more recorded signals, generating a dynamic system model for some aspect of the WPTS; and using the generated dynamic system model in combination with stored training data to determine whether an object having characteristics distinguishing the object as a receiver coil is near the transmit coil.

The invention discloses an aircraft flutter prediction and analysis method and device. The method comprises the following steps: applying an input signal through an exciting point on an aircraft structure, and receiving an output signal through a collection point which is configured on the aircraft structure; establishing a nonlinear kinetic model of the aircraft structure by utilizing a nonlinear system recognition algorithm according to the input signal and the output signal; establishing an unsteady aerodynamic model on the basis of the calculation fluid kinetic technology; performing reduced order for the unsteady aerodynamic model according to the nonlinear kinetic model of the aircraft structure to obtain a reduced order model; performing the fluid-structure interaction analysis on the nonlinear kinetic model and the reduced order model so as to make the flutter prediction and analysis on the corresponding aircraft structure. Since the nonlinear kinetic model of the aircraft structure is acquired by performing the test according to the real aircraft structure, the accuracy in flutter prediction and analysis can be improved.

The invention relates to an arrangement and a method for the identification of parameters in a nonlinear model of an analog-to-digital converter (ADC 17) and the use of this information to reduce the nonlinear distortions of the ADC. A parameter estimator determines an AD parameter vector PAD which describes the nonlinearities of the ADC (17). According to the invention the ADC is excited by a perturbed input signal y′A generated by adder 77 which combines the analog input signal yA with perturbation signal s1 provided by generator. The nonlinear system identification uses intermodulation distortion generated in the digital ADC output signal y′D which are not found in the analog input signal yA. A nonlinear AD compensation element compensates based on parameter vector PAD the nonlinear distortion generated by the ADC and generates the linearized output signal yD. Digital control information PP,1 are used to generate the perturbation signal s1 in generator and to remove the perturbation signal the compensation element. The linearized ADC (is the basis for linearizing digital-analog-converters (DAC), amplifiers and other hardware components.

The invention discloses a method and device for identifying frequency and damping ratio of main components of wind power generator. The method comprises the following steps of: obtaining the input data and output data of the main components of a wind power generator system, and using the input and output data respectively as the input and output of a fan model established by nonlinear system identification; calculating the model parameters of the fan model and a transfer function of the main components; calculating the pole of the transfer function, obtaining a first frequency range, performing power spectrum analysis on the output data of the main components, obtaining a second frequency range, and narrowing the range of the first frequency range by using the second frequency range to obtain the frequency iteration range; calculating the amplitude corresponding to each frequency in the frequency iteration range, and obtaining the maximum amplitude, the target frequency corresponding to the maximum amplitude and the target pole p, calculating the damping ratio of the main components by formula as shown in the specification. The above technical solution disclosed in the present application can improve the accuracy of frequency and damping ratio identification of main components, and the identification method is simple and easy to operate.

The invention discloses a self-organization method of a feedforward neural network structure based on neuron salience, relates to the technical field of neural networks, and aims at the problem of difficult selection of a traditional feedforward neural network structure, the method proposed by the invention is based on hidden layer neurons The significance size dynamically adjusts the network structure of the neural network. Experimental results show that the improved algorithm can reduce the blindness of network structure selection, realize the dynamic optimization and adjustment of network structure, and improve the accuracy of network recognition. It has high accuracy in nonlinear system identification, data classification and engineering defect type identification.

The invention provides a speaker nonlinear system identification method, comprising the following steps: providing an amplified excitation signal to the speaker system to be tested; synchronously measuring voltage signals and current signals at both ends of the speaker system; obtaining linear parameters of the speaker system: According to the measured voltage signal and the measured current signal, under large signal conditions, calculate the impedance curve of the speaker system, and use the least square method to match the impedance curve to obtain the linear parameters of the speaker system; obtain the nonlinear parameters of the speaker system : Input the measured current signal into the lumped parameter model of the loudspeaker system to calculate the estimated voltage signal; compare the estimated voltage signal with the measured voltage signal, and calculate the voltage error signal between the two; calculate the voltage error signal In decoherence, the linear component in the voltage error signal is removed, and then according to the voltage error signal after decoherence, an adaptive iterative algorithm is used to obtain nonlinear parameters.

The invention discloses a nonlinear system recognizing method based on multi-target genetic programming. The method mainly overcomes the shortcomings of low recognizing accuracy and low solving speed of the traditional recognizing method under the condition that the structure of a nonlinear system is unknown. A plurality of target optimization function models optimizing the structure of the nonlinear system and the system parameters are provided. A novel multi-target genetic programming method is adopted to solve the model. A novel evaluation method aiming at multiple optimization targets is provided, a decision making process is blended in the optimization process, calculation of multiple Pareto optimum solutions is not required, and the evaluation process is high in efficiency. The method can effectively excavate the nonlinear relation between a large amount of input-output data, and improve recognizing efficiency and accuracy.

The invention provides a nonlinear system identification method based on structure adaptive filtering, which comprises the following steps: obtaining input data and corresponding output data of an identification object as a data set, and dividing the data set into a training set and a test set; constructing a self-adaptive filtering model: randomly selecting a first-order subsystem and a second-order subsystem according to the generation parameters, and randomly cascading the subsystems according to a connection rule to form a complete self-adaptive filtering model; initializing an evolutionary algorithm, and selecting an optimal adaptive filtering model; carrying out loop iteration through an evolutionary algorithm, and finding the optimal structure and parameters of the optimal adaptivefiltering model; and testing by using the trained adaptive filtering model to obtain the actual output of the identification object. According to the method, the nonlinear process of the typical liquid saturated steam heat exchanger is learned by utilizing the nonlinear adaptive filtering model, and the optimal estimation can be found; the method is simple in operation, small in error, high in precision and few in used parameters, and has remarkable nonlinear system recognition capability.

The invention discloses a maximum length sequence optimization method and system for nonlinear system identification; the system comprises an m-sequence generating unit, a displacement function calculating unit, an allowable desirable section length calculating unit, and an optimal m-sequence selecting unit. The method comprises: generating corresponding m-sequences according to the preset order of a primitive polynomial; calculating displacement function and allowable desirable section length corresponding to each m-sequence; selecting allowable desirable section length with maximum value from the corresponding allowable desirable section lengths of all the m-sequences. By using the method and system of the invention, it is possible to effectively avoid overlap distortion possibly occurring during calculation of core sectioning, and identification accuracy is greatly improved. The maximum length sequence optimization method and system for nonlinear system identification are widely applicable to the field of biomedical signal treatment.

The invention discloses a method for identifying a Wiener nonlinear system based on parameter separation, comprising the following steps: A. converting a thermal power plant control system to be identified into a Wiener nonlinear system, and performing an input quantity of the thermal power plant control system to be identified Merge; B. Analyze the Wiener nonlinear system, including the linear dynamic part structure of the system, the nonlinear static part structure of the system, the dynamic disturbance type, and the measurement noise; determine n a , n b and n c , set the initial value, and collect the input data u(k) and y(k) repeatedly until k≥n a +n, k≥n b +n; C. Separate the time-invariant parameters and time-varying parameters of the Wiener nonlinear system; D. Identify the Wiener nonlinear system; E. When the identified model does not meet the requirements, return to step A and readjust the model structure and initial values, re-identify until a system model that meets the requirements is obtained. The invention can improve the deficiencies of the prior art, and improves the identification accuracy and convergence speed of the Wiener nonlinear system.

The invention relates to a simple grey-box space identification method for a Hammerstein nonlinear industrial system driven by dynamic and static data, and belongs to the field of control theory and control engineering nonlinear system identification. The method includes: S1: collect dynamic data and static data of the system; S2: select and process the Hammerstein nonlinear system model to obtain a prediction model; S3: construct a dynamic parsimonious model of the Hammerstein nonlinear system; S4: construct a static parsimonious model of the Hammerstein nonlinear system ; S5: Use the hierarchical Lagrangian optimal weighting method to solve the system parameter fusion identification. The present invention simultaneously adopts dynamic and static data, based on a decomposed parsimonious model, avoids the estimation of additional intermediate parameters, reduces the variance of the estimated model, and improves the accuracy of the model.