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Stochastic resonance in an overdamped monostable system with multiplicative and additive α stable noise
Author(s) -
Shangbin Jiao,
Ren Chao,
Penghua Li,
Qing Zhang,
Guo Xie
Publication year - 2014
Publication title -
acta physica sinica
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.63.070501
Subject(s) - multivibrator , stochastic resonance , noise (video) , multiplicative function , multiplicative noise , control theory (sociology) , resonance (particle physics) , physics , stability (learning theory) , statistical physics , mathematics , computer science , mathematical analysis , quantum mechanics , telecommunications , control (management) , signal transfer function , voltage , artificial intelligence , transmission (telecommunications) , machine learning , analog signal , image (mathematics)
In this paper we combine α stable noise with a monostable stochastic resonance (SR) system to investigate the overdamped monostable SR phenomenon with multiplicative and additive α stable noise, and explore the action laws of the stability index α (0 α ≤ 2) and skewness parameter β (-1 ≤ β ≤ 1) of the α stable noise, the monostable system parameter a, and the amplification factor D of the multiplicative α stable noise against the resonance output effect. Results show that for different distributions of α stable noise, the single or multiple low-and high-frequency weak signals detection can be realized by adjusting the parameter a or D within a certain range. For a or D, respectively, there is an optimal value which can make the system produce the best SR effect. Different α or β can regularly change the system resonance output effect. Moreover, when α or β is given different values, the evolution laws in the monostable SR system excited by low-and high-frequency weak signals are the same. The conclusions drawn for the study of single-and multi-frequency monostable SR with α stable noise are also the same. These results will be the foundation for realizing the adaptive parameter adjustment in the monostable SR system with α stable noise.

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