Finite-time synchronization of delayed memristive neural networks via 1-norm-based analytical approach

被引：7

作者：

Zhang, Wanli ^{[1
]}

Yang, Shiju ^{[1
]}

Li, Chuandong ^{[1
]}

Li, Hongfei ^{[1
]}

机构：

[1] Southwest Univ, Natl & Local Joint Engn Lab Intelligent Transmiss, Coll Elect & Informat Engn, Chongqing 400715, Peoples R China

来源：

NEURAL COMPUTING & APPLICATIONS | 2020年 / 32卷 / 09期

基金：

中国国家自然科学基金;

关键词：

Memristive neural networks; Finite-time synchronization; Time-varying delay; 1-Norm-based analytical approach; Quantized controller; EXPONENTIAL SYNCHRONIZATION; CHAOTIC SYSTEMS; STABILITY; CONSENSUS; DYNAMICS;

D O I：

10.1007/s00521-018-3906-2

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

By using 1-norm-based analytical approach, this paper considers finite-time (FET) synchronization for memristive neural networks (MNNs) with time-varying delays. New quantized controllers are designed, which can save communication channel and play an important role in synchronizing MNNs. By constructing Lyapunov function, and developing 1-norm-based analytical methods, several conditions are derived to guarantee that the MNNs can be synchronized within a settling time. In addition, the settling time is also presented for the considered MNNs. Some numerical simulations are provided to illustrate the theoretical results.

引用

页码：4951 / 4960

页数：10

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