期刊
IEEE TRANSACTIONS ON CYBERNETICS
卷 50, 期 7, 页码 2959-2970出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCYB.2019.2912890
关键词
Memristors; Stability analysis; Control theory; Neural networks; Delay effects; Synchronization; Finite-time stabilization; fuzzy logics; hybrid time delays; Lagrange stability; memristive neural networks (MNNs)
类别
资金
- Newton Advanced Fellowship Award of the Royal Society [NA160545]
- Natural Science Foundation of China [61673188, 61761130081]
- Innovation Group Project of the National Natural Science Foundation of China [61821003]
- National Key Research and Development Program of China [2016YFB0800402]
- Foundation for Innovative Research Groups of Hubei Province of China [2017CFA005]
- 111 Project on Computational Intelligence and Intelligent Control [B18024]
- Post-Doctoral Innovation Talent Support Program of China [BX20180107]
- PostDoctoral Science Foundation of China [2018M640700]
This paper focuses on Lagrange exponential stability and finite-time stabilization of Takagi-Sugeno (T-S) fuzzy memristive neural networks with discrete and distributed time-varying delays (DFMNNs). By resorting to theories of differential inclusions and the comparison strategy, an algebraic condition is developed to confirm Lagrange exponential stability of the underlying DFMNNs in Filippov's sense, and the exponentially attractive set is estimated. When external input is not considered, global exponential stability of DFMNNs is derived directly, which includes some existing ones as special cases. Furthermore, finite-time stabilization of the addressed DFMNNs is analyzed by exploiting inequality techniques and the comparison approach via designing a nonlinear state feedback controller. The boundedness assumption of activation functions is removed herein. Finally, two simulations are presented to demonstrate the validness of the outcomes, and an application is performed in pseudorandom number generation.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据