Nonfragile Finite-Time Synchronization for Coupled Neural Networks With Impulsive Approach

被引：36

作者：

Rao, Hongxia ^{[1
]}

Guo, Yuru ^{[1
]}

Xu, Yong ^{[1
]}

Liu, Chang ^{[1
]}

Lu, Renquan ^{[1
]}

机构：

[1] Guangdong Univ Technol, Sch Automat, Guangdong Prov Key Lab Intelligent Decis & Cooper, Guangzhou 510006, Peoples R China

来源：

IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS | 2020年 / 31卷 / 11期

基金：

中国国家自然科学基金;

关键词：

Artificial neural networks; Synchronization; Couplings; Robustness; Learning systems; Biological neural networks; Stochastic processes; Finite-time boundedness; impulsive control; neural networks (NNs); nonfragile controller; synchronization; STATE ESTIMATION; STABILITY; DISCRETE;

D O I：

10.1109/TNNLS.2020.3001196

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

This article addresses the problem of the average stochastic finite-time synchronization (ASFTS) for a set of coupled neural networks (NNs) with energy-bounded noises. Due to the channel capacity constraint, the impulsive approach is introduced so as to cut down the communication times among the leader NNs and the follower NNs. Then, a nonfragile controller is designed to improve the robustness of the controller with randomly occurred uncertainty. The sufficient conditions that guarantee the ASFTS of the coupled NNs and the leader NNs are achieved. The boundary of the synchronization error is also obtained by constructing the monotonic increasing functions. Finally, the controller gains are given based on the derived conditions, and their effectiveness is illustrated by a numerical example.

引用

页码：4980 / 4989

页数：10

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