Optimization control for mean square synchronization of stochastic semi-Markov jump neural networks with non-fragile hidden information and actuator saturation.

Abstract

This paper studies the asynchronous output feedback control and H_∞ synchronization problems for a class of continuous-time stochastic hidden semi-Markov jump neural networks (SMJNNs) affected by actuator saturation. Initially, a novel neural networks (NNs) model is constructed, incorporating semi-Markov process (SMP), hidden information, and Brownian motion to accurately simulate the complexity and uncertainty of real-world environments. Secondly, acknowledging system mode mismatches and the need for robust anti-interference capabilities, a non-fragile controller based on hidden information is proposed. The designed controller effectively mitigates the impact of uncertainties enhancing system reliability. Furthermore, sufficient conditions for stochastic mean square synchronization (MSS) within the domain of attraction are provided, and optimal control is achieved through the construction of a Lyapunov function based on SMP. Finally, the feasibility of the proposed method is verified through numerical examples.