The dynamic behavior of the periodic potential system driven by the cross-correlated non-Gaussian noise and Gaussian white noise is studied in this article. According to path integral method and unified color noise approximation, the periodic potential system is transformed into a stochastic equivalent Stratonovich stochastic differential equation. Then the Fokker-Planck equation and the expression of the steady-state probability density are derived.The fourth-order Runge-Kutta algorithm is used to calculate the 5 × 10 4 times response of the system. Meanwhile, the probability density function (PDF) of the first-passage time (FPT) is simulated, and the mean first-passage time (MFPT) is obtained by averaging these values. Finally, the influence of noise parameters on MFPT and PDF of FPT is analyzed.
In this paper, the first-passage behavior of under-damped asymmetric bistable system driven by Lévy noise is studied. The two aspects considered are the mean first-passage time (MFPT) and the distribution of first-passage time in two opposite directions. To begin with, using the Janicki–Weron algorithm to generate Lévy noise, the system driven by Lévy noise is simulated through the fourth-order Runge–Kutta algorithm. Then the first-passage time of [Formula: see text] response tracks is calculated, and the MFPT and the distribution of first-passage time are obtained. Finally, the influence of Lévy noise and system parameters on MFPT and the distribution of first-passage time are analyzed. Moreover, the noise enhanced stability (NES) effect is found.
In this paper, the stochastic resonance (SR) in a periodic potential system driven by cross-correlated noises and periodic signal is investigated. The signal-to-noise ratio (SNR) is used to characterize the SR. Using the algorithm of fourth-order Runge–Kutta, we obtain the curves of SNR for different parameters. The effects of some system parameters, additive Gaussian white noise and multiplicative Gaussian colored noise intensity on SR are characterized by analyzing SNR curves. When increasing system parameter and noise cross-correlation strength in SNR-D, the SR of the system can be enhanced. However, the SR will be weakened by increasing other parameters. Otherwise, the phenomena in SNR-Q are opposite to in SNR-D when increasing signal amplitude and correlation time.
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