Event-Triggered Synchronization of Multiple Fractional-Order Recurrent Neural Networks With Time-Varying Delays

被引：27
作者：
Liu, Peng [1 ,2 ]
Wang, Jun [3 ,4 ]
Zeng, Zhigang [5 ,6 ]
机构：
[1] Zhengzhou Univ Light Ind, Sch Elect & Informat Engn, Zhengzhou 450002, Peoples R China
[2] Henan Key Lab Informat Based Elect Appliances, Zhengzhou 450002, Peoples R China
[3] City Univ Hong Kong, Dept Comp Sci, Kowloon, Hong Kong, Peoples R China
[4] City Univ Hong Kong, Sch Data Sci, Kowloon, Hong Kong, Peoples R China
[5] Huazhong Univ Sci & Technol, Sch Artificial Intelligence & Automat, Wuhan 430074, Peoples R China
[6] Huazhong Univ Sci & Technol, Key Lab Image Proc & Intelligent Control, Educ Minist China, Wuhan 430074, Peoples R China
来源：
IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2023年 / 34卷 / 08期
基金：
中国国家自然科学基金;
关键词：
Synchronization; Protocols; Delays; Recurrent neural networks; Delay effects; Control systems; Optimization; Event-triggered communication; fractional-order systems; recurrent neural networks (RNNs); synchronization; GLOBAL EXPONENTIAL SYNCHRONIZATION; LEADER-FOLLOWING CONSENSUS; NEURODYNAMIC APPROACH; MULTIAGENT SYSTEMS; STABILITY;
D O I：
10.1109/TNNLS.2021.3116382
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 addresses the synchronization of multiple fractional-order recurrent neural networks (RNNs) with time-varying delays under event-triggered communications. Based on the assumption of the existence of strong connectivity or a spanning tree in the communication digraph, two sets of sufficient conditions are derived for achieving event-triggered synchronization. Moreover, an additional condition is derived to preclude Zeno behaviors. As a generalization of existing results, the criteria herein are also applicable to the event-triggered synchronization of multiple integer-order RNNs with or without delays. Two numerical examples are elaborated to illustrate the new results.
引用
收藏
页码：4620 / 4630
页数：11
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