Anti-Quasisynchronization for Asynchronous Leader–Follower Markovian Neural Networks With Hidden Markov Model-Based Intermittent Control
研究了参数不匹配的离散时间异步领导者-跟随者马尔可夫神经网络的抗准同步问题，利用隐马尔可夫模型设计间歇非脆弱控制器，并通过数值仿真验证了有效性。
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.