Boundedness and periodicity for linear threshold discrete-time quaternion-valued neural network with time-delays

被引：50

作者：

Hu, Jin ^{[1
]}

Zeng, Chunna ^{[2
]}

Tan, Jun ^{[1
]}

机构：

[1] Chongqing Jiaotong Univ, Dept Math, Chongqing, Peoples R China

[2] Chongqing Normal Univ, Coll Math, Chongqing, Peoples R China

来源：

NEUROCOMPUTING | 2017年 / 267卷

基金：

中国国家自然科学基金;

关键词：

Quaternion-valued neural networks; Linear threshold; boundedness; Periodicity; ASSOCIATIVE MEMORY; COMPLETE STABILITY; ROBUST STABILITY; EQUATIONS; NEURONS;

D O I：

10.1016/j.neucom.2017.06.047

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, the discrete-time quaternion-valued neural network with linear threshold activation functions is investigated. The sufficient conditions to the boundedness and global exponential periodicity of the neural network are obtained by using characteristic equation, Lyapunov functional and M-matrix. Simulation results illustrative the effectiveness of the conclusions obtained in this paper. (C) 2017 Elsevier B.V. All rights reserved.

引用

页码：417 / 425

页数：9

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