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首页> 外文期刊>International Journal of Innovative Computing Information and Control >NONFRAGILE MEMORY-BASED OUTPUT FEEDBACK CONTROL FOR FUZZY MARKOV JUMP GENERALIZED NEURAL NETWORKS WITH REACTION-DIFFUSION TERMS
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NONFRAGILE MEMORY-BASED OUTPUT FEEDBACK CONTROL FOR FUZZY MARKOV JUMP GENERALIZED NEURAL NETWORKS WITH REACTION-DIFFUSION TERMS

机译：基于非货币基于内存的输出反馈控制，用于模糊Markov Jump广义神经网络具有反应扩散术语

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摘要

This paper investigates the stabilization issue of T-S fuzzy Markov jump generalized neural networks (GNNs) with reaction-diffusion terms. A nonfragile memory-based control strategy that contains a constant signal transmission delay is proposed. Additionally, the controller gain optimization method and the principle for the number of selected variables in the derived process are also analyzed in this paper. Firstly, based on the original T-S fuzzy Markov jump GNNs, a full-order observer with designed controller is established. Then the stable criteria of the considered error system are proposed and two relevant corollaries are also derived. Finally, three numerical examples are given to demonstrate the validity of the related results and the superiority of the designed controller.
机译：本文研究了与反应扩散术语的T-S模糊马尔可夫跳跃广义神经网络（GNNS）的稳定问题。提出了一种包含恒定信号传输延迟的基于非免费内存的控制策略。另外，本文还分析了控制器增益优化方法和衍生过程中所选变量数量的原理。首先，基于原始T-S模糊马尔可夫跳跃GNN，建立了一种设计控制器的全阶观察者。然后提出了所考虑的误差系统的稳定标准，也得到了两个相关的冠状动因。最后，给出了三个数值例子来证明相关结果的有效性和设计控制器的优越性。

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