Non-Fragile H ∞ Asynchronous State Estimation for Delayed Markovian Jumping NNs with Stochastic Disturbance

Lan Wang, Juping Tang, Qiang Li (), Xianwei Yang and Haiyang Zhang
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Lan Wang: Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China
Juping Tang: Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China
Qiang Li: School of Information and Artificial Intelligence, Anhui Agricultural University, Hefei 230036, China
Xianwei Yang: Department of Fundamental Courses, Wuxi University of Technology, Wuxi 214121, China
Haiyang Zhang: School of Mathematics and Computer Science, Yunnan Minzu University, Kunming 650500, China

Mathematics, 2025, vol. 13, issue 15, 1-17

Abstract: This article focuses on tackling the non-fragile H ∞ asynchronous estimation problem for delayed Markovian jumping neural networks (NNs) featuring stochastic disturbance. To more accurately reflect real-world scenarios, external random disturbances with known statistical characteristics are incorporated. Through the integration of stochastic analysis theory and Lyapunov stability techniques, as well as several matrix constraints formulas, some sufficient and effective results are addressed. These criteria ensure that the considered NNs achieve anticipant H ∞ stability in line with an external disturbance mitigation level. Meanwhile, the expected estimator gains will be explicitly constructed by dealing with corresponding matrix constraints. To conclude, a numerical simulation example is offered to showcase workability and validity of the formulated estimation method.

Keywords: neural networks; H ∞ estimation; stochastic disturbance; time-varying delay; matrix inequalities (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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