Stochastic resonance in a non-smooth system under colored noise excitations with a controllable parameter

2018 ◽  
Vol 28 (7) ◽  
pp. 073104 ◽  
Author(s):  
Youming Lei ◽  
Haohao Bi ◽  
Huiqing Zhang
Keyword(s):  
Stochastic Resonance ◽  
Colored Noise ◽  
Controllable Parameter ◽  
Smooth System

Stochastic resonance and MFPT in an asymmetric bistable system driven by correlated multiplicative colored noise and additive white noise

2017 ◽  
Vol 31 (14) ◽  
pp. 1750113 ◽  
Author(s):  
Pei-Ming Shi ◽  
Qun Li ◽  
Dong-Ying Han
Keyword(s):  
White Noise ◽  
Stochastic Resonance ◽  
Colored Noise ◽  
First Passage Time ◽  
Passage Time ◽  
State Theory ◽  
Bistable System ◽  
Periodic Signal ◽  
Additive White Noise ◽  
Correlation Strength

This paper investigates a new asymmetric bistable model driven by correlated multiplicative colored noise and additive white noise. The mean first-passage time (MFPT) and the signal-to-noise ratio (SNR) as the indexes of evaluating the model are researched. Based on the two-state theory and the adiabatic approximation theory, the expressions of MFPT and SNR have been obtained for the asymmetric bistable system driven by a periodic signal, correlated multiplicative colored noise and additive noise. Simulation results show that it is easier to generate stochastic resonance (SR) to adjust the intensity of correlation strength [Formula: see text]. Meanwhile, the decrease of asymmetric coefficient [Formula: see text] and the increase of noise intensity are beneficial to realize the transition between the two steady states in the system. At the same time, the twice SR phenomena can be observed by adjusting additive white noise and correlation strength. The influence of asymmetry of potential function on the MFPTs in two different directions is different.

scholarly journals Stochastic Resonance with Colored Noise for Neural Signal Detection

PLoS ONE ◽  
2014 ◽  
Vol 9 (3) ◽  
pp. e91345 ◽  
Author(s):  
Fabing Duan ◽  
François Chapeau-Blondeau ◽  
Derek Abbott
Keyword(s):  
Signal Detection ◽  
Stochastic Resonance ◽  
Colored Noise ◽  
Neural Signal

Asymmetric stochastic resonance under non-Gaussian colored noise and time-delayed feedback

2020 ◽  
Vol 29 (5) ◽  
pp. 050501 ◽  
Author(s):  
Ting-Ting Shi ◽  
Xue-Mei Xu ◽  
Ke-Hui Sun ◽  
Yi-Peng Ding ◽  
Guo-Wei Huang
Keyword(s):  
Stochastic Resonance ◽  
Colored Noise ◽  
Delayed Feedback ◽  
Gaussian Colored Noise ◽  
Non Gaussian ◽  
Time Delayed Feedback

scholarly journals Observation of stochastic resonance in a liquid-crystal light valve with optical feedback induced by colored noise in the driving voltage

2020 ◽  
Vol 102 (6) ◽  
Author(s):  
Yoshitomo Goto ◽  
Atsuya Shishibe ◽  
Hiroshi Orihara ◽  
Stefania Residori ◽  
Tomoyuki Nagaya
Keyword(s):  
Liquid Crystal ◽  
Stochastic Resonance ◽  
Colored Noise ◽  
Optical Feedback ◽  
Driving Voltage ◽  
Light Valve

Entropic Stochastic Resonance Driven by Colored Noise

2010 ◽  
Vol 27 (4) ◽  
pp. 040503 ◽  
Author(s):  
Zhao Liang ◽  
Luo Xiao-Qin ◽  
Wu Dan ◽  
Zhu Shi-Qun ◽  
Gu Ji-Hua
Keyword(s):  
Stochastic Resonance ◽  
Colored Noise

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.

Sign in / Sign up

Export Citation Format

Share Document