Parametric study and multi-parameter optimization of a generalized second-order tri-stable stochastic resonance system

The weak signal extraction methods based on stochastic resonance (SR) are extensively studied due to their ability of utilizing noise energy to enhance weak signals. Therefore, many SR-based methods have been proposed to extract characteristic signals from strong background noise. Among various SR m...

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Vydáno v:Nonlinear dynamics Ročník 112; číslo 4; s. 2661 - 2681
Hlavní autoři: Zhang, Cailiang, Lai, Zhihui, Zhao, Yifan, Zhu, Ronghua
Médium: Journal Article
Jazyk:angličtina
Vydáno: Dordrecht Springer Netherlands 01.02.2024
Springer Nature B.V
Témata:
Algorithms
Automotive Engineering
Background noise
Classical Mechanics
Control
Damping ratio
Dynamical Systems
Engineering
Mathematical models
Mechanical Engineering
Optimization
Original Paper
Parameters
Particle swarm optimization
Performance evaluation
Signal to noise ratio
Stochastic resonance
Vibration
Signal-to-noise ratio
Mean first-passage time
Particle swarm optimization
Generalized second-order tri-stable stochastic resonance
Characteristic signal recognition rate
ISSN:0924-090X, 1573-269X
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Shrnutí:The weak signal extraction methods based on stochastic resonance (SR) are extensively studied due to their ability of utilizing noise energy to enhance weak signals. Therefore, many SR-based methods have been proposed to extract characteristic signals from strong background noise. Among various SR models, the second-order tri-stable SR models have demonstrated their superiority in weak signal extraction with better output performance compared to other competitive SR models. However, the current studies have two main shortcomings including nonstandard forms of the system equation and insufficient investigations on the system parameters. Therefore, a generalized second-order tri-stable SR (GSTSR) system is proposed in this paper to address the aforementioned issues. In order to evaluate the output performance of the GSTSR system, the mean first-passage time (MFPT) of the system is derived, and the signal-to-noise ratio (SNR) and the so-called characteristic signal recognition rate (CSRR) are designed. On this basis, the effects of the parameters of the GSTSR (including the asymmetric parameters, the damping ratio and the scale-transformation parameters) on the system’s SR performance are fully investigated through numerical simulations. Furthermore, the multi-parameter optimization algorithm of the GSTSR system based on particle swarm optimization (PSO) is conducted, and the optimization results are analyzed. The research results can provide guidance for designing the tri-stable SR models with a superior SR performance.
Bibliografie:ObjectType-Article-1
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ISSN:0924-090X
1573-269X
DOI:10.1007/s11071-023-09211-1