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

IF 6 1区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Neural Networks Pub Date : 2024-11-23 DOI:10.1016/j.neunet.2024.106942

Zou Yang, Jun Wang, Kaibo Shi, Xiao Cai, Sheng Han

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引用次数: 0

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.

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来源期刊

Neural Networks 工程技术-计算机：人工智能

CiteScore

13.90

自引率

7.70%

发文量

425

审稿时长

67 days

期刊介绍： Neural Networks is a platform that aims to foster an international community of scholars and practitioners interested in neural networks, deep learning, and other approaches to artificial intelligence and machine learning. Our journal invites submissions covering various aspects of neural networks research, from computational neuroscience and cognitive modeling to mathematical analyses and engineering applications. By providing a forum for interdisciplinary discussions between biology and technology, we aim to encourage the development of biologically-inspired artificial intelligence.