The Experts below are selected from a list of 324 Experts worldwide ranked by ideXlab platform
Jianguo Tan - One of the best experts on this subject based on the ideXlab platform.
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The mean first-passage time in simplified FitzHugh–Nagumo neural model driven by correlated non-Gaussian Noise and Gaussian Noise
Modern Physics Letters B, 2018Co-Authors: Yongfeng Guo, Fang Wei, Jianguo TanAbstract:In this paper, the mean first-passage time (MFPT) in simplified FitzHugh–Nagumo (FHN) neural model driven by correlated multiplicative non-Gaussian Noise and additive Gaussian white Noise is studie...
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Multiplicative non-Gaussian Noise and additive Gaussian white Noise induced transition in a piecewise nonlinear model
Chinese Journal of Physics, 2017Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan, Ming LiuAbstract:Abstract We study the transition problems in a piecewise nonlinear model induced by correlated multiplicative non-Gaussian Noise and additive Gaussian white Noise. Firstly, applying the path integral approach, the unified colored Noise approximation, the analytical expression of the steady-state probability density function (SPD) is derived. Then the change regulation of the SPD is analyzed with the change of the strength and relevance of multiplicative Noise and additive Noise. From numerical computations we obtain some new nonlinear phenomena: the transition can be induced by the cross-correlation strength between Noises, the non-Gaussian Noise intensity and the Gaussian Noise intensity as well as the non-Gaussian Noise deviation parameter. This indicates that the effect of the non-Gaussian Noise intensity on SPD is the same as that of the Gaussian Noise intensity. Moreover, we also find the correlation time of the non-Gaussian Noise can not induce the transition.
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Stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian Noise and additive white Noise
Communications in Nonlinear Science and Numerical Simulation, 2016Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo TanAbstract:The phenomenon of stochastic resonance (SR) in a piecewise nonlinear model driven by a periodic signal and correlated Noises for the cases of a multiplicative non-Gaussian Noise and an additive Gaussian white Noise is investigated. Applying the path integral approach, the unified colored Noise approximation and the two-state model theory, the analytical expression of the signal-to-Noise ratio (SNR) is derived. It is found that conventional stochastic resonance exists in this system. From numerical computations we obtain that: (i) As a function of the non-Gaussian Noise intensity, the SNR is increased when the non-Gaussian Noise deviation parameter q is increased. (ii) As a function of the Gaussian Noise intensity, the SNR is decreased when q is increased. This demonstrates that the effect of the non-Gaussian Noise on SNR is different from that of the Gaussian Noise in this system. Moreover, we further discuss the effect of the correlation time of the non-Gaussian Noise, cross-correlation strength, the amplitude and frequency of the periodic signal on SR.
Bidhan Chandra Bag - One of the best experts on this subject based on the ideXlab platform.
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Colored non-Gaussian Noise induced resonant activation
Chemical Physics Letters, 2005Co-Authors: Pradip Majee, Gurupada Goswami, Bidhan Chandra BagAbstract:Abstract We have investigated the mean first passage time (MFPT) problem in the presence of a fluctuating barrier. The fluctuation is carried out through colored multiplicative Noise. Both Gaussian and non-Gaussian colored Noises are considered. Our study shows that the resonant activation (RA) can appear at slower rate of increase of external Noise strength with Noise correlation time ( τ ) for non-Gaussian Noise compared to Gaussian Noise. When RA appears for both the Noises then minimum in plot of MFPT vs. τ occurs at greater value of τ for Gaussian Noise than that for non-Gaussian Noise. Our calculation also shows that rate of decrease of the MFPT with increasing strength of additive white Noise, is faster for Gaussian colored multiplicative Noise compared to corresponding for non-Gaussian case.
Yongfeng Guo - One of the best experts on this subject based on the ideXlab platform.
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The mean first-passage time in simplified FitzHugh–Nagumo neural model driven by correlated non-Gaussian Noise and Gaussian Noise
Modern Physics Letters B, 2018Co-Authors: Yongfeng Guo, Fang Wei, Jianguo TanAbstract:In this paper, the mean first-passage time (MFPT) in simplified FitzHugh–Nagumo (FHN) neural model driven by correlated multiplicative non-Gaussian Noise and additive Gaussian white Noise is studie...
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Multiplicative non-Gaussian Noise and additive Gaussian white Noise induced transition in a piecewise nonlinear model
Chinese Journal of Physics, 2017Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan, Ming LiuAbstract:Abstract We study the transition problems in a piecewise nonlinear model induced by correlated multiplicative non-Gaussian Noise and additive Gaussian white Noise. Firstly, applying the path integral approach, the unified colored Noise approximation, the analytical expression of the steady-state probability density function (SPD) is derived. Then the change regulation of the SPD is analyzed with the change of the strength and relevance of multiplicative Noise and additive Noise. From numerical computations we obtain some new nonlinear phenomena: the transition can be induced by the cross-correlation strength between Noises, the non-Gaussian Noise intensity and the Gaussian Noise intensity as well as the non-Gaussian Noise deviation parameter. This indicates that the effect of the non-Gaussian Noise intensity on SPD is the same as that of the Gaussian Noise intensity. Moreover, we also find the correlation time of the non-Gaussian Noise can not induce the transition.
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Stochastic resonance in a piecewise nonlinear model driven by multiplicative non-Gaussian Noise and additive white Noise
Communications in Nonlinear Science and Numerical Simulation, 2016Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo TanAbstract:The phenomenon of stochastic resonance (SR) in a piecewise nonlinear model driven by a periodic signal and correlated Noises for the cases of a multiplicative non-Gaussian Noise and an additive Gaussian white Noise is investigated. Applying the path integral approach, the unified colored Noise approximation and the two-state model theory, the analytical expression of the signal-to-Noise ratio (SNR) is derived. It is found that conventional stochastic resonance exists in this system. From numerical computations we obtain that: (i) As a function of the non-Gaussian Noise intensity, the SNR is increased when the non-Gaussian Noise deviation parameter q is increased. (ii) As a function of the Gaussian Noise intensity, the SNR is decreased when q is increased. This demonstrates that the effect of the non-Gaussian Noise on SNR is different from that of the Gaussian Noise in this system. Moreover, we further discuss the effect of the correlation time of the non-Gaussian Noise, cross-correlation strength, the amplitude and frequency of the periodic signal on SR.
Pradip Majee - One of the best experts on this subject based on the ideXlab platform.
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Colored non-Gaussian Noise induced resonant activation
Chemical Physics Letters, 2005Co-Authors: Pradip Majee, Gurupada Goswami, Bidhan Chandra BagAbstract:Abstract We have investigated the mean first passage time (MFPT) problem in the presence of a fluctuating barrier. The fluctuation is carried out through colored multiplicative Noise. Both Gaussian and non-Gaussian colored Noises are considered. Our study shows that the resonant activation (RA) can appear at slower rate of increase of external Noise strength with Noise correlation time ( τ ) for non-Gaussian Noise compared to Gaussian Noise. When RA appears for both the Noises then minimum in plot of MFPT vs. τ occurs at greater value of τ for Gaussian Noise than that for non-Gaussian Noise. Our calculation also shows that rate of decrease of the MFPT with increasing strength of additive white Noise, is faster for Gaussian colored multiplicative Noise compared to corresponding for non-Gaussian case.
Andrey A. Chabanov - One of the best experts on this subject based on the ideXlab platform.
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Intensity statistics of random signals in Gaussian Noise.
Physical Review E, 2009Co-Authors: Andrey A. ChabanovAbstract:The intensity statistics of random signals in the presence of Gaussian Noise is obtained by considering the model of a random signal plus a random phasor sum. The additive Gaussian Noise is shown to result in a Bessel transform of the probability density of signal intensity. The transformation of the intensity statistics can generally be applied to mixtures of independent random signals, one of which being a complex-valued Gaussian random process. It is used to retrieve intensity statistics of microwave pulsed transmission from Gaussian Noise at long time delays.