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
收藏
页码：4951 / 4960
页数：10
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