Academic Report of School of Mathematical Sciences [2026] No. 101

(Series Report for High-Level University Construction No. 1360)

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

Speaker：Associate Professor Li Zhibao (Central South University）

Time：11:00-12:00, Oct. 26, 2026

Location：Tencent Meeting: 465628393

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.

Speaker Profile：Li Zhibao is an associate professor and master’s thesis advisor in the School of Mathematics and Statistics at Central South University. His research interests include optimization theory and methods, signal processing for sensor arrays, and stochastic resonant acoustics. He has published more than 30 academic papers in journals such as Inverse Problems, Applied Mathematical Modelling, Journal of Computational and Applied Mathematics, Engineering Applications of Artificial Intelligence, IEEE/ACM Transactions on Audio, Speech, and Language Processing, and Digital Signal Processing. He has (previously) led projects such as the General Program and Young Investigators Program of the National Natural Science Foundation of China, the General Program and Young Investigators Program of the Hunan Provincial Natural Science Foundation, and the Changsha Municipal Science and Technology Program.

Faculty and students are welcome to attend!

Invited by: Tu Kai

School of Mathematical Sciences

October 23, 2026