Abstract

This paper focuses on the state estimation issue of T–S fuzzy Markovian generalized neural networks (GNNs) with reaction–diffusion terms. An estimator-based nonfragile time-varying proportional retarded sampled-data controller that permits norm-bounded indeterminacy and contains a time-varying delay is designed to guarantee the asymptotical stability of the error system. By establishing a novel Lyapunov–Krasovskii functional that involves positive indefinite items and discontinuous items, meanwhile, by combining the reciprocally convex combination method, Jenson’s inequality and Wirtinger inequality, a less conservative stability criterion can be derived. Moreover, the principle for the number of selected variables in the process of deriving main results is also analyzed. Finally, two numerical examples are given to demonstrate the validity and advantages of the results proposed in this paper.

中文翻译：

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具有反应扩散项的TS模糊Markovian广义神经网络的状态估计：时变非脆弱比例滞后采样数据控制方案

摘要

本文着重研究带有反应扩散项的T–S模糊马尔可夫广义神经网络（GNN）的状态估计问题。设计一种基于估计器的非脆弱时变比例延迟采样数据控制器，该控制器允许范数有界的不确定性并包含时变延迟，旨在确保误差系统的渐近稳定性。通过建立一个新颖的Lyapunov–Krasovskii泛函，它涉及正不确定项和不连续项，同时，通过将双向凸组合方法，Jenson不等式和Wirtinger不等式相结合，可以得出较不保守的稳定性准则。此外，还分析了在得出主要结果的过程中选择变量的数量的原理。最后，