基于隐马尔可夫模型间歇控制的异步Leader-Follower马尔可夫神经网络的反准同步。

IF 9.4 1区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

IEEE Transactions on Cybernetics Pub Date : 2025-07-16 DOI:10.1109/tcyb.2025.3582043

Zijing Xiao,Meng Zhang,Hongxia Rao,Chang Liu,Yong Xu

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引用次数: 0

摘要

研究了参数不匹配的离散时间异步leader-follower马尔可夫神经网络的反准同步问题。为了克服能量约束，引入了间歇控制传输策略。同时，为了解决leader-follower MNNs中未知马尔可夫模型的挑战，利用隐马尔可夫模型（HMM）从可观测信息中推断未知模式。然后，针对随动mnn设计了基于HMM的间歇非脆弱控制器。利用指数迭代法建立了leader-follower MNNs抗准同步的充分条件，得到了抗准同步的最优边界。最后，通过数值仿真验证了所提出的基于hmm的间歇控制器的有效性。

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

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.

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来源期刊

IEEE Transactions on Cybernetics COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, CYBERNETICS

CiteScore

25.40

自引率

11.00%

发文量

1869

期刊介绍： The scope of the IEEE Transactions on Cybernetics includes computational approaches to the field of cybernetics. Specifically, the transactions welcomes papers on communication and control across machines or machine, human, and organizations. The scope includes such areas as computational intelligence, computer vision, neural networks, genetic algorithms, machine learning, fuzzy systems, cognitive systems, decision making, and robotics, to the extent that they contribute to the theme of cybernetics or demonstrate an application of cybernetics principles.