Global Synchronization of Coupled Fractional-Order Recurrent Neural Networks

被引：103
|
作者：
Liu, Peng [1 ,2 ]
Zeng, Zhigang [3 ,4 ]
Wang, Jun [5 ]
机构：
[1] Zhengzhou Univ Light Ind, Coll Elect & Informat Engn, Zhengzhou 450002, Henan, Peoples R China
[2] Henan Key Lab Informat Based Elect Appliances, Zhengzhou 450002, Henan, Peoples R China
[3] Huazhong Univ Sci & Technol, Sch Automat, Wuhan 430074, Hubei, Peoples R China
[4] Minist China, Key Lab Image Proc & Intelligent Control Educ, Wuhan 430074, Hubei, Peoples R China
[5] City Univ Hong Kong, Sch Data Sci, Dept Comp Sci, Hong Kong, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2019年 / 30卷 / 08期
关键词：
Fractional-order recurrent neural networks; sequential connectivity; synchronization; LAG SYNCHRONIZATION; LYAPUNOV FUNCTIONS; STABILITY ANALYSIS; CONSENSUS; CALCULUS; SYSTEMS; DELAY; IMAGE;
D O I：
10.1109/TNNLS.2018.2884620
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 global synchronization of coupled fractional-order recurrent neural networks. Under the assumptions that the coupled fractional-order recurrent neural networks are sequentially connected in form of a single spanning tree or multiple spanning trees, two sets of sufficient conditions are derived for ascertaining the global synchronization by using the properties of Mittag-Leffler function and stochastic matrices. Compared with existing works, the results herein are applicable for fractional-order systems, which could be viewed as an extension of integer-order ones. Two numerical examples are presented to illustrate the effectiveness and characteristics of the theoretical results.
引用
收藏
页码：2358 / 2368
页数：11
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