Multiperiodicity and Attractivity Analysis for Discrete-Time Second Order Delayed Recurrent Neural Networks

被引：9
|
作者：
Qiu, Fang [1 ]
机构：
[1] Binzhou Univ, Dept Math & Informat Sci, Binzhou 256603, Shandong, Peoples R China
来源：
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING | 2013年 / 38卷 / 05期
关键词：
Second order recurrent neural network; Discrete-time; Multiperiodicity; Attractivity; Periodic orbit; EXPONENTIAL STABILITY; GLOBAL STABILITY; VARYING DELAYS; GENERAL-CLASS; MULTISTABILITY;
D O I：
10.1007/s13369-013-0603-4
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
This paper investigates the multiperiodicity and attractivity of discrete-time second order recurrent neural networks. Sufficient conditions are obtained for the existence of 2(n) locally attractive periodic orbits in an n-dimensional network. Also, the state space is divided into 3(n) regions and sufficient conditions are given to ensure that a periodic orbit belonging to one of these regions is locally or globally attractive. As particular cases, cellular neural networks and Hopfield neural networks are also investigated. Some numerical simulations are given which remarkably demonstrate the theoretical results.
引用
收藏
页码：1269 / 1280
页数：12
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