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.

  • Multiplicative Non-Gaussian Noise and additive Gaussian white Noise induced transition in a piecewise nonlinear model
    Chinese Journal of Physics, 2017
    Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan, Ming Liu
    Abstract:

    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.

  • Stochastic resonance in a piecewise nonlinear model driven by multiplicative Non-Gaussian Noise and additive white Noise
    Communications in Nonlinear Science and Numerical Simulation, 2016
    Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan
    Abstract:

    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.

Yongfeng Guo - One of the best experts on this subject based on the ideXlab platform.

  • Dynamical Behavior of Brusselator System Driven by Non-Gaussian Noise
    American Scientific Research Journal for Engineering Technology and Sciences, 2020
    Co-Authors: Qiang Dong, Yongfeng Guo
    Abstract:

    The Non-Gaussian Noise induced the mean first passage time (MFPT) in Brusselator system are examined. In this paper, the path integral method is used to approximate Non-Gaussian Noise to Gaussian color Noise. The FPT of the 50000 response tracks is obtained by solving the system equation through the fourth-order stochastic Runge-Kutta algorithm. Then we get the MFPT. The influences of the Noise intensity, correlation time and Non-Gaussian parameter of Non-Gaussian Noise on the MFPT are characterized. We also found the Noise enhanced stability (NES) phenomenon in the system.

  • Multiplicative Non-Gaussian Noise and additive Gaussian white Noise induced transition in a piecewise nonlinear model
    Chinese Journal of Physics, 2017
    Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan, Ming Liu
    Abstract:

    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.

  • Stochastic resonance in a piecewise nonlinear model driven by multiplicative Non-Gaussian Noise and additive white Noise
    Communications in Nonlinear Science and Numerical Simulation, 2016
    Co-Authors: Yongfeng Guo, Ya-jun Shen, Jianguo Tan
    Abstract:

    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.

Dong-cheng Mei - One of the best experts on this subject based on the ideXlab platform.

  • Delay-enhanced stability and stochastic resonance in perception bistability under Non-Gaussian Noise
    Journal of Statistical Mechanics: Theory and Experiment, 2015
    Co-Authors: Tao Yang, Chunhua Zeng, Ruifen Liu, Hua Wang, Dong-cheng Mei
    Abstract:

    In this paper we investigate the effect of time delay in an attractor network model of perception bistability driven by Non-Gaussian Noise. Using delay Langevin and Fokker–Planck approaches, the theoretical analysis of the model is presented. It is found that the mean first-passage time (MFPT) as a function of the time delay exhibits a maximum, which is identified as the characteristic of the delay-enhanced stability of the system. This is different to the case of Noise-enhanced stability. The Non-Gaussian Noise-enhanced stability of the system is also analyzed. The signal-to-Noise ratio (SNR) as a function of the Noise intensity exhibits a maximum. This maximum implies the identifying characteristic of stochastic resonance (SR), and the time delay and Non-Gaussian Noise can enhance the SR phenomenon.

Akihisa Ichiki - One of the best experts on this subject based on the ideXlab platform.

  • Design and characterization of nonlinear functions for the transmission of a small signal with Non-Gaussian Noise.
    Physical review. E Statistical nonlinear and soft matter physics, 2013
    Co-Authors: Seiya Kasai, Yukihiro Tadokoro, Akihisa Ichiki
    Abstract:

    We design nonlinear functions for the transmission of a small signal with Non-Gaussian Noise and perform experiments to characterize their responses. Using statistical design theory [A. Ichiki and Y. Tadokoro, Phys. Rev. E 87, 012124 (2013)], a static nonlinear function is estimated from the probability density function of the given Noise in order to maximize the signal-to-Noise ratio of the output. Using an electronic system that implements the optimized nonlinear function, we confirm the recovery of a small signal from a signal with Non-Gaussian Noise. In our experiment, the Non-Gaussian Noise is a mixture of Gaussian Noises. A similar technique is also applied to the optimization of the threshold value of the function. We find that, for Non-Gaussian Noise, the response of the optimized nonlinear systems is better than that of the linear system.

Bidhan Chandra Bag - One of the best experts on this subject based on the ideXlab platform.

  • Colored Non-Gaussian Noise induced resonant activation
    Chemical Physics Letters, 2005
    Co-Authors: Pradip Majee, Gurupada Goswami, Bidhan Chandra Bag
    Abstract:

    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.

  • Colored Non-Gaussian Noise driven systems: Mean first passage time
    The European Physical Journal B - Condensed Matter, 2003
    Co-Authors: Bidhan Chandra Bag
    Abstract:

    We have examined the mean first passage time ( $\langle T\rangle$ ) for a particle driven by colored Non-Gaussian Noise. As we depart from the Gaussian behavior, the $\langle T\rangle$ decreases regularly to a limiting value, i.e., the barrier crossing rate can be accelerated to a limiting value by increasing the Non-Gaussianity of the Noise. For the Non-Gaussian Noise driven process $\langle T\rangle$ increases linearly with increasing damping constant or Noise correlation time. But this increasing behavior is almost exponential in nature for the Gaussian Noise driven process.