Exponential Stabilization of Fuzzy Memristive Neural Networks With Hybrid Unbounded Time-Varying Delays

被引：77

作者：

Sheng, Yin ^{[1
]}

Lewis, Frank L. ^{[2
]}

Zeng, Zhigang ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, Sch Automat, Guangdong HUST Ind Technol Res Inst, Guangdong Prov Key Lab Digital Mfg Equipment,Key, Wuhan 430074, Hubei, Peoples R China

[2] Univ Texas Arlington, UTA Res Inst, Arlington, TX 76118 USA

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2019年 / 30卷 / 03期

关键词：

Exponential stabilization; hybrid unbounded time delays; memristive neural networks (MNNs); Takagi-Sugeno; (T-S) fuzzy logics; DIRICHLET BOUNDARY-CONDITIONS; IMPULSIVE SYNCHRONIZATION; DYNAMICAL NETWORKS; GENERAL-CLASS; SAMPLED-DATA; STABILITY; PASSIVITY; DISCRETE; SYSTEMS; PASSIFICATION;

D O I：

10.1109/TNNLS.2018.2852497

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This paper is concerned with exponential stabilization for a class of Takagi-Sugeno fuzzy memristive neural networks (FMNNs) with unbounded discrete and distributed time-varying delays. Under the framework of Filippov solutions, algebraic criteria are established to guarantee exponential stabilization of the addressed FMNNs with hybrid unbounded time delays via designing a fuzzy state feedback controller by exploiting inequality techniques, calculus theorems, and theories of fuzzy sets. The obtained results in this paper enhance and generalize some existing ones. Meanwhile, a general theoretical framework is proposed to investigate the dynamical behaviors of various neural networks with mixed infinite time delays. Finally, two simulation examples are performed to illustrate the validity of the derived outcomes.

引用

页码：739 / 750

页数：12

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