Finite-time synchronizing control for chaotic neural networks
type: Item
instance of: scholarly article
title: Finite-time synchronizing control for chaotic neural networks
zbMATH Open document ID: 1406.93184
DOI: 10.1155/2014/938612
author: Chao Zhang
author: Qiang Guo
author: Jing Wang
published in: Abstract and Applied Analysis
publication date: 2019-02-14T00:00:00Z
review text: Summary: This paper addresses the finite-time synchronizing problem for a class of chaotic neural networks. In a real communication network, parameters of the master system may be time-varying and the system may be perturbed by external disturbances. A simple high-gain observer is designed to track all the nonlinearities, unknown system functions, and disturbances. Then, a dynamic active compensatory controller is proposed and by using the singular perturbation theory, the control method can guarantee the finite-time stability of the error system between the master system and the slave system. Finally, two illustrative examples are provided to show the effectiveness and applicability of the proposed scheme.
Mathematics Subject Classification ID: 93C41
Mathematics Subject Classification ID: 93C15
Mathematics Subject Classification ID: 68T05
Mathematics Subject Classification ID: 34H10
zbMATH DE Number: 7023352
Wikidata QID: Q59042975
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.1155/2014/938612
OpenAlex ID: W2166412270
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description: scientific article; zbMATH DE number 7023352