Stability analysis of neutral-type stochastic delayed neural networks with Markov switching

Xiaohan Nan; Mengdie Li; Xiaoqi Sun; Xiaohan Nan; Mengdie Li; Xiaoqi Sun

doi:10.3934/era.2026051

Electronic Research Archive

2026, Volume 34, Issue 2: 1095-1123. doi: 10.3934/era.2026051

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Stability analysis of neutral-type stochastic delayed neural networks with Markov switching

School of Mathematics and Statistics, Qingdao University, 308 Ningxia Road, Qingdao 266071, China

Received: 02 December 2025 Revised: 18 January 2026 Accepted: 26 January 2026 Published: 04 February 2026

This paper investigated the stability of a class of neutral-type stochastic delayed neural networks with Markov switching. Under a general decay rate and weaker conditions on the neutral term, sufficient conditions for stability in the p-th moment, almost sure stability, and actual stability were established by constructing appropriate Lyapunov functions and applying the nonnegative semimartingale convergence theorem. The theoretical analysis was validated via MATLAB simulations using the Euler-Maruyama method.
- neutral stochastic delayed neural networks,
- p-th moment stability,
- Markov switching,
- general decay,
- Lyapunov function
Citation: Xiaohan Nan, Mengdie Li, Xiaoqi Sun. Stability analysis of neutral-type stochastic delayed neural networks with Markov switching[J]. Electronic Research Archive, 2026, 34(2): 1095-1123. doi: 10.3934/era.2026051

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Abstract

This paper investigated the stability of a class of neutral-type stochastic delayed neural networks with Markov switching. Under a general decay rate and weaker conditions on the neutral term, sufficient conditions for stability in the p-th moment, almost sure stability, and actual stability were established by constructing appropriate Lyapunov functions and applying the nonnegative semimartingale convergence theorem. The theoretical analysis was validated via MATLAB simulations using the Euler-Maruyama method.

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