A high dimensional stochastic resonance system and its application in signal processing

2022 ◽  
Vol 154 ◽  
pp. 111642
Author(s):  
Zuanbo Zhou ◽  
Wenxin Yu ◽  
Junnian Wang ◽  
Meiting Liu
Keyword(s):  
Signal Processing ◽  
Stochastic Resonance ◽  
High Dimensional ◽  
Resonance System

Magnetic anomaly signal detection using adaptive stochastic resonance system

2021 ◽  
Author(s):  
Zhongyan Liu ◽  
Yujing Xu ◽  
Wang Liu ◽  
Qi Zhang ◽  
Jiafei Hu ◽  
...  
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Magnetic Anomaly ◽  
Resonance System

The Stochastic Resonance of Noise Frequency Modulation Signal Using Motion-Group Fourier Transform

2015 ◽  
Vol 713-715 ◽  
pp. 1452-1455
Author(s):  
Jing Bo He ◽  
Sheng Liang Hu
Keyword(s):  
Signal Processing ◽  
Fourier Transform ◽  
Stochastic Resonance ◽  
Frequency Modulation ◽  
Planck Equation ◽  
Motion Group ◽  
Noise Frequency ◽  
Modulation Signal ◽  
Group Fourier Transform ◽  
Linear Differential

In this paper stochastic resonance was studied in radar driven by noise frequency modulation signal. According to the intrinsic relations between the stochastic differential and the radar jamming signal processing, the stochastic calculus was used in the radar jamming signal processing in this paper. The noise frequency modulation signal was particularly analyzed. The Fokker-Planck equation of noise frequency modulation was presented and the Motion-Group Fourier Transform was used by converting the partial differential equation into the variable coefficient homogenous linear differential equations. Then the solutions were given.

NovelWoods–Saxon stochastic resonance system for weak signal detection

2020 ◽  
Vol 29 (4) ◽  
pp. 040503 ◽  
Author(s):  
Yong-Hui Zhou ◽  
Xue-Mei Xu ◽  
Lin-Zi Yin ◽  
Yi-Peng Ding ◽  
Jia-Feng Ding ◽  
...  
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Weak Signal ◽  
Weak Signal Detection ◽  
Resonance System

scholarly journals Analysis of Asymmetric Piecewise Linear Stochastic Resonance Signal Processing Model Based on Genetic Algorithm

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-11
Author(s):  
Lina He ◽  
Chuan Jiang
Keyword(s):  
Genetic Algorithm ◽  
Fault Detection ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Piecewise Linear ◽  
Poor Performance ◽  
System Model ◽  
Adaptive Genetic Algorithm ◽  
Resonance System ◽  
Noise Energy

The stochastic resonance system has the advantage of making the noise energy transfer to the signal energy. Because the existing stochastic resonance system model has the problem of poor performance, an asymmetric piecewise linear stochastic resonance system model is proposed, and the parameters of the model are optimized by a genetic algorithm. The signal-to-noise ratio formula of the model is derived and analyzed, and the theoretical basis for better performance of the model is given. The influence of the asymmetric coefficient on system performance is studied, which provides guidance for the selection of initial optimization range when a genetic algorithm is used. At the same time, the formula is verified and analyzed by numerical simulation, and the correctness of the formula is proved. Finally, the model is applied to bearing fault detection, and an adaptive genetic algorithm is used to optimize the parameters of the system. The results show that the model has an excellent detection effect, which proves that the model has great potential in fault detection.

scholarly journals Actuator Weak Fault Diagnosis in Autonomous Underwater Vehicle Based on Tri-Stable Stochastic Resonance

2020 ◽  
Vol 10 (6) ◽  
pp. 2048 ◽  
Author(s):  
Yang Jiang ◽  
Bo He ◽  
Jia Guo ◽  
Pengfei Lv ◽  
Xiaokai Mu ◽  
...  
Keyword(s):  
Fault Diagnosis ◽  
Stochastic Resonance ◽  
Autonomous Underwater Vehicle ◽  
Underwater Vehicle ◽  
Ant Colony ◽  
Resonance System ◽  
Resonance Model ◽  
Weak Fault

The autonomous underwater vehicle (AUV) is mainly used in the development and exploration of the ocean. As an important module of the AUV, the actuator plays an important role in the normal execution of the AUV. Therefore, the fault diagnosis of the actuator is particularly important. At present, the research on the strong faults, such as the winding of the actuator, has achieved good results, but the research on the weak fault diagnosis is relatively rare. In this paper, the tri-stable stochastic resonance model is analyzed, and the ant colony tri-stable stochastic resonance model is used to diagnose the weak fault. The system accurately diagnoses the fault of the actuator collision and verifies the adaptive tri-stable stochastic resonance system. This model has better diagnostic results than the bi-stable stochastic resonance system.

A Novel Adaptive Stochastic Resonance Method Based on Tristable System and its Applications

2020 ◽  
pp. 2150004
Author(s):  
Gang Zhang ◽  
Chuan Jiang ◽  
Tian Qi Zhang
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Piecewise Linear ◽  
Correlation Coefficients ◽  
Resonance Method ◽  
Resonance System ◽  
Signal Energy ◽  
Noise Energy ◽  
Novel Model ◽  
Output Snr

Stochastic resonance systems have the advantages of converting noise energy into signal energy, and have great potential in the field of signal detection and extraction. Aiming at the problems of the performance of classical stochastic resonance system whose model is not perfect enough and the correlation coefficients between parameters is too large to be optimized by algorithm, then a novel model of the tristable potential stochastic resonance system is proposed. The output SNR formula of the model is derived and analyzed, and the influence of its parameters on the model is clarified. Compared with the piecewise linear model by numerical simulation, the correctness of the formula and the superiority of the model are verified. Finally, the model and the classical tristable model are applied to bearing fault detection in which the genetic algorithm is used to optimize the parameters of the two systems. The results show that the model has better detection effects, which prove that the model has a strong potential in the field of signal detection.

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