On the optimal design of stochastic resonance system based on Bayesian optimization

Zhida Chen, Wenqi Huang, Zhibao Li and Hongqiao Wang

Chaos, Solitons & Fractals, 2026, vol. 210, issue P2

Abstract: A stochastic resonance (SR) system is typically formulated using the Langevin equation to model a bistable or multistable nonlinear system. The equilibria of the system are determined by its parameters, which play a crucial role in overall performance, particularly in high-dimensional SR systems. In this paper, we focus on the optimal design of a four-dimensional SR system derived from a modified Lorenz force chaotic system. This modification introduces additional adjustable parameters, increasing the total to eight, which enhances detection performance, improves signal processing flexibility, and enables adaptation to diverse application scenarios. To determine the optimal parameter configuration, we employ the signal-to-noise ratio improvement (SNRI) to evaluate signal detection performance and guide the development of a Bayesian optimization-based parameter selection algorithm. By maximizing SNRI, we identify the optimal system configuration. Additionally, we introduce specific metrics to assess the reliability of SR systems when detecting weak signals subject to characteristic frequency disturbances. Experimental results indicate that the proposed algorithm exhibits superior performance across diverse challenging scenarios, thereby establishing a robust basis for subsequent enhancements to SR models.

Keywords: Stochastic resonance; Weak signal detection; Bayesian optimization; Reliability assessment (search for similar items in EconPapers)
Date: 2026
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0960077926008684
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:chsofr:v:210:y:2026:i:p2:s0960077926008684

DOI: 10.1016/j.chaos.2026.118727

Access Statistics for this article

Chaos, Solitons & Fractals is currently edited by Stefano Boccaletti and Stelios Bekiros

More articles in Chaos, Solitons & Fractals from Elsevier
Bibliographic data for series maintained by Thayer, Thomas R. ().