scholarly journals Stochastic Resonance in a Multistable System Driven by Gaussian Noise

2016 ◽  
Vol 2016 ◽  
pp. 1-7 ◽  
Author(s):  
Peiming Shi ◽  
Pei Li ◽  
Shujun An ◽  
Dongying Han
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
State Theory ◽  
Elimination Theory ◽  
Resonance System ◽  
System Parameters ◽  
Nonmonotonic Function

Stochastic resonance (SR) is investigated in a multistable system driven by Gaussian white noise. Using adiabatic elimination theory and three-state theory, the signal-to-noise ratio (SNR) is derived. We find the effects of the noise intensity and the resonance system parametersb,c, anddon the SNR; the results show that SNR is a nonmonotonic function of the noise intensity; therefore, a multistable SR is found in this system, and the value of the peak changes with changing the system parameters.

STOCHASTIC RESONANCE FOR A METAPOPULATION SYSTEM SUBJECTED TO CORRELATED COLORED NOISES

2013 ◽  
Vol 27 (18) ◽  
pp. 1350136 ◽  
Author(s):  
KANG-KANG WANG ◽  
XIAN-BIN LIU ◽  
SHENG-HONG LI
Keyword(s):  
Stochastic Resonance ◽  
Noise Intensity ◽  
Multiplicative Noise ◽  
Signal To Noise Ratio ◽  
State Theory ◽  
Descent Method ◽  
Correlated Noise ◽  
Signal To Noise ◽  
System Parameters ◽  
Fast Descent

In the present paper, for a Levins metapopulation system that is driven by correlated colored noises, the phenomenon of stochastic resonance (SR) is investigated. Based on the two-state theory and by the use of fast descent method, the expression of the signal-to-noise ratio (SNR) is obtained. Via a numerical simulation, it is shown that the conventional SR occurs in the Levins model for the different values of system parameters. And furthermore, it is revealed that, under the different conditions that if the correlation intensities between the two noises are different, i.e. positive or negative, then all the effects of the addictive noise intensity, the multiplicative noise intensity, the correlated noise intensity and the correlation time on SNR are different.

Stochastic resonance in FitzHugh–Nagumo neural system driven by correlated non-Gaussian noise and Gaussian noise

2017 ◽  
Vol 31 (32) ◽  
pp. 1750264 ◽  
Author(s):  
Yong-Feng Guo ◽  
Bei Xi ◽  
Fang Wei ◽  
Jian-Guo Tan
Keyword(s):  
Stochastic Resonance ◽  
Gaussian Noise ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Neural System ◽  
Integral Approach ◽  
Stationary Probability Distribution ◽  
Non Gaussian ◽  
Two State Model

In this paper, the phenomenon of stochastic resonance in FitzHugh–Nagumo (FHN) neural system driven by correlated non-Gaussian noise and Gaussian white noise is investigated. First, the analytical expression of the stationary probability distribution is derived by using the path integral approach and the unified colored noise approximation. Then, we obtain the expression of signal-to-noise ratio (SNR) by applying the theory of two-state model. The results show that the phenomena of stochastic resonance and multiple stochastic resonance appear in FHN neural system under different values of parameters. The effects of the multiplicative noise intensity D and the additive noise intensity Q on the SNR are entirely different. In addition, the discharge behavior of FHN neural system is restrained when the value of Q is smaller. But, it is conducive to enhance signal response of FHN neural system when the values of Q and D are relatively larger.

INVESTIGATION OF STOCHASTIC RESONANCE IN MONOSTABLE SYSTEMS

2008 ◽  
Vol 18 (09) ◽  
pp. 2833-2839 ◽  
Author(s):  
N. V. AGUDOV ◽  
A. V. KRICHIGIN
Keyword(s):  
Stochastic Resonance ◽  
Output Signal ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Periodic Signal ◽  
Signal Power ◽  
Signal To Noise ◽  
Input Noise ◽  
Power Amplification

The phenomena of stochastic resonance is studied in overdamped nonlinear monostable systems driven by a periodic signal and Gaussian white noise. It is shown that the signal power amplification as a function of input noise intensity can be different depending on nonlinearity: it can monotonically grow, decrease and it can reach a maximum at certain value of the noise intensity. Nevertheless, the output signal to noise ratio is shown to be always a decreasing function of input noise intensity.

THE CRITICAL EFFECTS OF TIME DELAY AND NOISE CORRELATION ON STOCHASTIC RESONANCE IN AN ASYMMETRIC BISTABLE SYSTEM

2011 ◽  
Vol 25 (16) ◽  
pp. 1377-1391 ◽  
Author(s):  
ZHENG-LIN JIA ◽  
DONG-CHENG MEI
Keyword(s):  
Time Delay ◽  
Stochastic Resonance ◽  
Noise Intensity ◽  
Signal To Noise Ratio ◽  
State Theory ◽  
Bistable System ◽  
Noise Correlation ◽  
Small Delay ◽  
Correlation Strength ◽  
Two Peaks

We investigate the effects of time delay and noise correlation on the stochastic resonance induced by a multiplicative signal in an asymmetric bistable system. By the two-state theory and small delay approximation, the expression of the output signal-to-noise ratio (SNR) is obtained in the adiabatic limit. The results show that SNR as a function of the multiplicative noise intensity D shows a transition from two peaks to one peak with the decreasing of cross-correlation strength λ and the increasing of delay time τ. Moreover, there are the doubly critical phenomena for SNR versus λ and τ, and SNR versus D and α (additive noise intensity).

scholarly journals Parallel Array Bistable Stochastic Resonance System with Independent Input and Its Signal-to-Noise Ratio Improvement

2014 ◽  
Vol 2014 ◽  
pp. 1-14 ◽  
Author(s):  
Wei Li ◽  
Hanzhi Lu ◽  
Yanyan Zuo
Keyword(s):  
Stochastic Resonance ◽  
Performance Improvement ◽  
Signal To Noise Ratio ◽  
Gaussian White Noise ◽  
Bistable System ◽  
Resonance System ◽  
Signal To Noise ◽  
Parallel Array ◽  
Independent Components ◽  
Noise Ratio

We study the design enhancement of the bistable stochastic resonance (SR) performance on sinusoidal signal and Gaussian white noise. The bistable system is known to show an SR property; however the performance improvement is limited. Our work presents two main contributions: first, we proposed a parallel array bistable system with independent components and averaged output; second, we give a deduction of the output signal-to-noise ratio (SNR) for this system to show the performance. Our examples show the enhancement of the system and how different parameters influence the performance of the proposed parallel array.

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.

Stochastic Resonance in FitzHugh-Nagumo Neural Model

2013 ◽  
Vol 415 ◽  
pp. 298-302
Author(s):  
Deng Rong Zhou ◽  
Jian Chun Gong ◽  
Dan Li
Keyword(s):  
Stochastic Resonance ◽  
Adiabatic Approximation ◽  
Multiplicative Noise ◽  
Additive Noise ◽  
Signal To Noise Ratio ◽  
Neural Model ◽  
System Parameters ◽  
Maximum Value ◽  
External Incentives ◽  
Delayed Time

Stochastic resonance is a non-linear phenomenon where the output response of the dynamic system reaches the maximum value under the joint action of a certain intensity of noises and external incentives. In this paper, the phenomenon of stochastic resonance in a FitzHugh-Nagumo neural (FHN) model is studied. For the case that the frequency of the HF signal is much higher than that of the LF signal, under the adiabatic approximation condition, the expression of the signal-to-noise ratio (SNR) with respect to the LF signal is obtained. It is shown that, the SNR is a non-monotonous function of the amplitude and frequency of the HF signal. In addition, the SNR varies non-monotonically with increasing the intensities of the multiplicative and additive noise, with increasing the delayed-time as well as increasing the system parameters of the FHN model. The influence of the correlation time of the colored multiplicative noise and the influence of the coupling strength between the multiplicative and additive noise on the SNR is discussed.

The Low Concentration Gas Detection Based on Genetic Algorithm Stochastic Resonance

2015 ◽  
Vol 738-739 ◽  
pp. 413-416
Author(s):  
Ji Jun Tong ◽  
Yan Qin Kang
Keyword(s):  
Genetic Algorithm ◽  
Stochastic Resonance ◽  
Signal To Noise Ratio ◽  
Optimization Theory ◽  
Gas Detection ◽  
Maximum Output ◽  
Resonance System ◽  
Detection Ability ◽  
Low Concentrations ◽  
The Relationship

The stochastic resonance (SR) theory provides a new idea for the detection of weak signal submerged in the strong noise. Combined with the optimization theory, this paper puts forward a stochastic resonance system based on genetic algorithm and applied it in a low concentrations gas detection. Firstly we preprocessed the input signal to satisfy the requirements of SR system, then developed the genetic algorithm to seek the maximum output signal-to-noise ratio (SNR), which was used to evaluate the performance of the system. In the end the relationship between the maximum SNR and concentration of gas was analyzed. The results of the experiments indicated the proposed method could improve the detection ability and enhance the detection limit of low gas concentrations.

Signal Recovery Based on Bistable Stochastic Resonance

2008 ◽  
Author(s):  
Yong-gang Leng ◽  
Yan Guo ◽  
Ying Zhang
Keyword(s):  
Numerical Simulation ◽  
Stochastic Resonance ◽  
Bistable System ◽  
Signal Recovery ◽  
Resonance System ◽  
System Parameters ◽  
Recovery Mechanism ◽  
Pulse Distortion ◽  
Waveform Distortion ◽  
Recovery System

This paper deals with the problem of the waveform distortion in the output of a bistable stochastic resonance system. The waveform recovery formula is proposed by observing the movement track of a particle in the bistable system and the suggested recovery system. The recovery mechanism and rules with and without noise are revealed. Moreover, a novel explanation about pulse distortion caused by the particle’s transitions occurring at the wells’ inflexions is put forward. Under the stochastic influence of the noise in subthreshold stochastic resonance, we develop a method via cascaded-bistable stochastic resonance and the recovery system with tuned system parameters to restore the output waveform. The numerical simulation has presented that it can recover the waveform containing weak information submerged in noise effectively. The method is applicable to both periodic and aperiodic signals.

scholarly journals Stochastic Resonance in Neuronal Network Motifs with Ornstein-Uhlenbeck Colored Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-7
Author(s):  
Xuyang Lou
Keyword(s):  
Stochastic Resonance ◽  
Correlation Time ◽  
Noise Intensity ◽  
Colored Noise ◽  
Stochastic Dynamics ◽  
Signal To Noise Ratio ◽  
Network Motif ◽  
Control Parameters ◽  
Noise Process ◽  
Chemical Coupling

We consider here the effect of the Ornstein-Uhlenbeck colored noise on the stochastic resonance of the feed-forward-loop (FFL) network motif. The FFL motif is modeled through the FitzHugh-Nagumo neuron model as well as the chemical coupling. Our results show that the noise intensity and the correlation time of the noise process serve as the control parameters, which have great impacts on the stochastic dynamics of the FFL motif. We find that, with a proper choice of noise intensities and the correlation time of the noise process, the signal-to-noise ratio (SNR) can display more than one peak.

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