Attractivity Analysis of Memristor-Based Cellular Neural Networks With Time-Varying Delays

被引：153
|
作者：
Guo, Zhenyuan [1 ,2 ]
Wang, Jun [2 ]
Yan, Zheng [2 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[2] Chinese Univ Hong Kong, Dept Mech & Automat Engn, Hong Kong, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2014年 / 25卷 / 04期
基金：
中国国家自然科学基金;
关键词：
Attractivity; cellular neural network; equilibrium; invariance; memristor; PERIODIC EXTERNAL INPUTS; ACTIVATION FUNCTIONS; EXPONENTIAL STABILITY; MULTISTABILITY; CIRCUIT; MULTIPERIODICITY; SYNCHRONIZATION; NEURONS; MEMORY; SYNAPSES;
D O I：
10.1109/TNNLS.2013.2280556
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 presents new theoretical results on the invariance and attractivity of memristor-based cellular neural networks (MCNNs) with time-varying delays. First, sufficient conditions to assure the boundedness and global attractivity of the networks are derived. Using state-space decomposition and some analytic techniques, it is shown that the number of equilibria located in the saturation regions of the piecewise-linear activation functions of an n-neuron MCNN with time-varying delays increases significantly from 2(n) to 2(2n2+n) (2(2n2) times) compared with that without a memristor. In addition, sufficient conditions for the invariance and local or global attractivity of equilibria or attractive sets in any designated region are derived. Finally, two illustrative examples are given to elaborate the characteristics of the results in detail.
引用
收藏
页码：704 / 717
页数：14
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