Finite time stability of periodic solution for Hopfield neural networks with discontinuous activations

被引：22
作者：
Chen, Xiaoyan [1 ]
Huang, Lihong [1 ,2 ]
Guo, Zhenyuan [1 ]
机构：
[1] Hunan Univ, Coll Math & Econometr, Changsha 410082, Hunan, Peoples R China
[2] Hunan Womens Univ, Changsha 410004, Hunan, Peoples R China
来源：
NEUROCOMPUTING | 2013年 / 103卷
基金：
中国国家自然科学基金;
关键词：
Hopfield neural network; Periodic solution; Finite time stability; Filippov theory; GLOBAL EXPONENTIAL STABILITY; CONVERGENCE; EXISTENCE;
D O I：
10.1016/j.neucom.2012.08.026
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
Based on the tangency or non-tangency of the periodic solution to certain surface, this paper gives a set of conditions ensuring global convergence in finite time toward a unique periodic solution for Hopfield neural networks with discontinuous activations. Moreover, two numerical examples are provided to illustrate the theoretical results. (c) 2012 Elsevier B.V. All rights reserved.
引用
收藏
页码：43 / 49
页数：7
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