A stochastic resonance simulation system

Abstract

This invention relates to the field of stochastic resonance, specifically to a stochastic resonance simulation system, comprising: a stochastic resonance parameter generation module, used to match a corresponding stochastic resonance simulation parameter table to the characteristic parameters of the current frequency signal; a stochastic resonance simulation module, used to build a bistable system stochastic resonance model based on Simulink, and adaptively select the corresponding stochastic resonance simulation parameters from the stochastic resonance simulation parameter table to complete the stochastic resonance simulation; a virtual parameter module, which is a logic unit inserted into the bistable system stochastic resonance model that can directly obtain the corresponding results or information; and a stochastic resonance simulation circuit, used to perform denoising processing on the current frequency signal based on the optimal simulation parameters obtained by the stochastic resonance simulation module. This invention can adaptively and quickly find the most suitable stochastic resonance simulation parameters according to different frequency signal enhancement targets, while improving the accuracy of stochastic resonance simulation results.

Description

Technical Field

[0001] This invention relates to the field of stochastic resonance, and more specifically to a stochastic resonance simulation system. Background Technology

[0002] In communication and signal processing systems, signal-to-noise ratio (SNR) is a crucial indicator of system performance. Research has found that SNR can be improved by preprocessing signals using stochastic resonance. Stochastic resonance is a nonlinear phenomenon where weak periodic signals and nonlinear systems interact through noise. When a certain match exists between the input signal, noise, and the nonlinear system, the power of the noise is transferred to the signal energy, resulting in an output SNR greater than the input SNR. Thus, noise can be used to enhance the signal.

[0003] The selection of parameters in a stochastic resonance simulation system plays a decisive role in the quality of the stochastic resonance method. Existing stochastic resonance systems generally adopt the optimal parameter selection method, which cannot adapt to different frequency signal enhancement requirements, thus limiting the application of stochastic resonance. At the same time, the influence of the stochastic resonance circuit conditions on the stochastic resonance simulation results is ignored during the simulation process, which reduces the accuracy of the stochastic resonance simulation results to some extent. Summary of the Invention

[0004] To address the aforementioned problems, this invention provides a stochastic resonance simulation system that can adaptively and quickly find the most suitable stochastic resonance simulation parameters based on different frequency signal enhancement targets, while simultaneously improving the accuracy of stochastic resonance simulation results.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] A stochastic resonance simulation system, comprising:

[0007] The random resonance parameter generation module is used to match the corresponding random resonance simulation parameter table to the characteristic parameters of the current frequency signal.

[0008] The stochastic resonance simulation module is used to build a stochastic resonance model of a bistable system based on Simulink. It adaptively selects the corresponding stochastic resonance simulation parameters from the stochastic resonance simulation parameter table to complete the stochastic resonance simulation.

[0009] The virtual parameter module is a logical unit inserted into the stochastic resonance model of a bistable system that can directly obtain the corresponding results or information.

[0010] The stochastic resonance simulation circuit is used to denoise the current frequency signal based on the optimal simulation parameters obtained from the stochastic resonance simulation module.

[0011] The intelligent terminal is used to compare and analyze the input and output characteristics of the current frequency signal and output the results of the comparison and analysis.

[0012] Furthermore, the stochastic resonance parameter generation module generates a corresponding stochastic resonance parameter simulation table based on the characteristic parameters of the current frequency signal and the signal enhancement target using a neural network model.

[0013] Furthermore, the stochastic resonance simulation module carries an adaptive control model, which is used to adaptively select the stochastic resonance parameters most likely to achieve the signal enhancement target from the stochastic resonance simulation parameter table.

[0014] Furthermore, each time the stochastic resonance simulation module performs a stochastic resonance simulation, the virtual parameter module automatically outputs the corresponding signal enhancement result. The adaptive control model adaptively selects the stochastic resonance parameters most likely to reach the target signal enhancement range for the next stochastic resonance simulation based on the changes in the identified adjacent signal enhancement results and the characteristic difference between the parameters and the target signal enhancement range.

[0015] Furthermore, it also includes:

[0016] The analog circuit operating condition access module is used to input the operating condition parameters of the stochastic resonance simulation circuit to avoid the results of the stochastic resonance simulation being affected by abnormal operating condition parameters of the stochastic circuit.

[0017] Furthermore, it also includes:

[0018] The stochastic resonance parameter aggregation module is used to aggregate the relevant parameters for each stochastic resonance simulation, thereby expanding the neural network model.

[0019] Furthermore, it also includes:

[0020] The human-computer interaction module is used to input the characteristic parameters of the current frequency signal and its corresponding signal enhancement target.

[0021] The present invention has the following beneficial effects:

[0022] 1) It can adaptively and quickly find the most suitable stochastic resonance simulation parameters based on different frequency signals to enhance the target, with a wide range of applicability;

[0023] 2) The introduction of an analog circuit operating condition access module avoids the occurrence of situations where abnormal operating parameters of random circuits affect the results of stochastic resonance simulation, thereby improving the accuracy of stochastic resonance simulation results. Attached Figure Description

[0024] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings:

[0025] Figure 1 This is a system block diagram of a stochastic resonance simulation system according to an embodiment of the present invention.

[0026] Figure 2 This is a flowchart of the stochastic resonance simulation in an embodiment of the present invention.

[0027] Figure 3 This is a schematic diagram illustrating the working principle of the analog circuit operating condition module in an embodiment of the present invention.

[0028] Figure 4 This is a flowchart illustrating the expansion of the neural network model in an embodiment of the present invention. Detailed Implementation

[0029] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of the present invention. These all fall within the scope of protection of the present invention. Example 1

[0030] like Figures 1-4 As shown, an embodiment of the stochastic resonance simulation system of the present invention includes:

[0031] The human-computer interaction module is used to input the characteristic parameters of the current frequency signal and its corresponding signal enhancement target. The characteristic parameters of the current frequency signal can be obtained by directly inputting the current frequency signal and its corresponding waveform. The signal enhancement target has two expression modes: one is the total harmonic distortion value, and the other is that it can be detected by a certain type of equipment. When a certain type of equipment is input, the web crawler module is activated to crawl the monitorable signal standard corresponding to the current type of equipment in the network base station, and then obtain the signal enhancement target.

[0032] The stochastic resonance parameter generation module is used to match the corresponding stochastic resonance simulation parameter table to the characteristic parameters of the current frequency signal. This module generates the corresponding stochastic resonance parameter simulation table based on a neural network model, taking into account the characteristic parameters of the current frequency signal and the signal enhancement target. Specifically, the neural network model is trained and constructed based on a preset stochastic resonance standard parameter table, which contains historical frequency signals and their corresponding stochastic resonance parameters and stochastic resonance signal enhancement results obtained through a combination of manual and machine learning methods.

[0033] The stochastic resonance simulation module is used to build a stochastic resonance model of a bistable system based on Simulink. It adaptively selects the corresponding stochastic resonance simulation parameters from the stochastic resonance simulation parameter table to complete the stochastic resonance simulation. The stochastic resonance simulation module carries an adaptive control model, which adaptively selects the stochastic resonance parameters most likely to achieve the signal enhancement target from the stochastic resonance simulation parameter table. Specifically, each time the stochastic resonance simulation module performs a stochastic resonance simulation, the virtual parameter module automatically outputs the corresponding signal enhancement result. The adaptive control model adaptively selects the stochastic resonance parameters most likely to reach the target signal enhancement range for the next stochastic resonance simulation based on the changes in the identified adjacent signal enhancement results and the characteristic difference between the target signal enhancement range and the target signal enhancement range, thereby shortening the acquisition time of the target stochastic resonance simulation parameters.

[0034] The virtual parameter module is a logical unit inserted into the stochastic resonance model of a bistable system that can directly obtain the corresponding results or information.

[0035] The stochastic resonance simulation circuit is used to denoise the current frequency signal based on the optimal simulation parameters obtained from the stochastic resonance simulation module.

[0036] The analog circuit operating condition access module is used to input the operating condition parameters of the random resonance simulation circuit to avoid affecting the results of the random resonance simulation due to abnormal operating condition parameters of the random circuit. Each random resonance simulation parameter corresponds to a standard random resonance simulation circuit operating condition parameter. When the received random resonance simulation circuit operating condition parameter deviates from the standard random resonance simulation circuit operating condition parameter, and the deviation is within the preset abnormal range, the random resonance simulation circuit operating condition is determined to be abnormal, the buzzer alarm sounds, and the intelligent terminal analyzes the abnormal random resonance simulation circuit operating condition and outputs maintenance suggestions for the random resonance simulation circuit.

[0037] The stochastic resonance parameter aggregation module is used to aggregate the relevant parameters of each stochastic resonance simulation, thereby expanding the neural network model. Each stochastic resonance simulation includes the simulation of the stochastic resonance simulation module and the simulation of the stochastic resonance simulation circuit. The relevant parameters for each simulation include at least the frequency signal and its corresponding stochastic resonance parameters and the stochastic resonance signal enhancement results.

[0038] The intelligent terminal is used to compare and analyze the input and output characteristics of the current frequency signal and output the results of the comparison and analysis. It has a built-in target model customization module, which is used to customize the comparison and analysis results according to the requirements, so that the corresponding comparison and analysis results, such as curves or a certain target parameter, can be directly obtained.

[0039] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. A stochastic resonance simulation system, characterized in that: include: The random resonance parameter generation module is used to match the corresponding random resonance simulation parameter table to the characteristic parameters of the current frequency signal. The stochastic resonance simulation module is used to build a stochastic resonance model of a bistable system based on Simulink. It adaptively selects the corresponding stochastic resonance simulation parameters from the stochastic resonance simulation parameter table to complete the stochastic resonance simulation. The virtual parameter module is a logical unit inserted into the stochastic resonance model of a bistable system that can directly obtain the corresponding results or information. The stochastic resonance simulation circuit is used to denoise the current frequency signal based on the optimal simulation parameters obtained from the stochastic resonance simulation module. The intelligent terminal is used to compare and analyze the input and output characteristics of the current frequency signal and output the results of the comparison and analysis. The stochastic resonance parameter generation module generates a corresponding stochastic resonance parameter simulation table based on a neural network model, according to the characteristic parameters of the current frequency signal and the signal enhancement target. The stochastic resonance simulation module carries an adaptive control model, which is used to adaptively select the stochastic resonance parameters most likely to achieve the signal enhancement target from the stochastic resonance simulation parameter table. Each time the stochastic resonance simulation module performs a stochastic resonance simulation, the virtual parameter module automatically outputs the corresponding signal enhancement result. The adaptive control model adaptively selects the stochastic resonance parameters most likely to reach the target signal enhancement range for the next stochastic resonance simulation based on the changes in the identified adjacent signal enhancement results and the feature difference with the target signal enhancement range. Also includes: The analog circuit operating condition access module is used to access the operating condition parameters of the stochastic resonance simulation circuit to avoid the results of the stochastic resonance simulation being affected by abnormal operating condition parameters of the stochastic circuit. Also includes: The stochastic resonance parameter aggregation module is used to aggregate the relevant parameters of each stochastic resonance simulation, thereby expanding the neural network model. Also includes: The human-computer interaction module is used to input the characteristic parameters of the current frequency signal and its corresponding signal enhancement target.

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