Anti-Quasisynchronization for Asynchronous Leader-Follower Markovian Neural Networks With Hidden Markov Model-Based Intermittent Control.

Journal: IEEE transactions on cybernetics
Published Date: Jul 16, 2025

Abstract

This study focuses on anti-quasisynchronization for discrete-time asynchronous leader-follower Markovian neural networks (MNNs) with mismatched parameters. To overcome the energy constraint, the intermittent control transmission strategy is introduced. Meanwhile, to address the challenge of unknown Markovian models in the leader-follower MNNs, a hidden Markov model (HMM) is utilized to infer unknown modes from observable information. Then, an intermittent nonfragile controller based on HMM is designed for the follower MNNs. Furthermore, the exponential iteration method is employed to establish sufficient conditions for ensuring anti-quasisynchronization for leader-follower MNNs, and an optimal boundary of anti-quasisynchronization is obtained. Ultimately, the effectiveness of the proposed HMM-based intermittent controller is demonstrated via a numerical simulation.

Authors

  • Zijing Xiao
  • Meng Zhang
    College of Software, Beihang University, Beijing, China.
  • Hongxia Rao
  • Chang Liu
    Key Lab of Cell Differentiation and Apoptosis of Ministry of Education, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
  • Yong Xu
    Department of Psychiatry, The First Hospital of Shanxi Medical University, Taiyuan, China.

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