Adaptive neural network prescribed performance matrix projection synchronization for unknown complex dynamical networks with different dimensions

被引：23

作者：

Fan, Aili ^{[1
]}

Li, Junmin ^{[1
]}

机构：

[1] Xidian Univ, Dept Appl Math, Xian 710071, Shaanxi, Peoples R China

来源：

NEUROCOMPUTING | 2018年 / 281卷

基金：

中国国家自然科学基金;

关键词：

Complex dynamic networks; Synchronization; Adaptive control; Prescribed performance; Different dimensions; NONLINEAR-SYSTEMS; FEEDBACK; OBSERVER; TOPOLOGY; NODES;

D O I：

10.1016/j.neucom.2017.11.055

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 investigates an adaptive neural network prescribed performance synchronization scheme for unknown complex dynamic networks with different dimensions. Based on predefined performance bounded and Lyapunov stability theory, adaptive neural networks controllers are designed to ensure that synchronization errors remain in a neighborhood of origin with the prescribed bounds. In addition, the paper analyses in detail that the synchronization behaviors between drive network selected as the three-dimension chaotic system and response network selected as the four-dimension hyperchaotic chaotic system. The numerical simulation results are presented to show the validity of the proposed approach. (c) 2017 Elsevier B.V. All rights reserved.

引用

页码：55 / 66

页数：12

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