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Neural Computing and Applications
Published in: Neural Computing and Applications 7-8/2014

01-12-2014 | Original Article

Exponential synchronization of two totally different chaotic systems based on a unified model

Authors: Meiqin Liu, Haiyang Chen, Senlin Zhang, Zhen Fan

Published in: Neural Computing and Applications | Issue 7-8/2014

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Abstract

This paper presents an exponential synchronization scheme between two chaotic systems with different structures and parameters. A unified model consisting of a linear dynamic system and a bounded static nonlinear operator is employed to describe these totally different chaotic systems. A novel state feedback control law is established to exponentially synchronize the two unified models with different parameters. Most chaotic systems with different structures and parameters, such as Hopfield neural networks, cellular neural networks, Chua’s circuits, unified chaotic systems, Qi systems, and chaotic recurrent multilayer perceptrons, can be transformed into this unified model with the synchronization controller designed in a unified way. Two numerical examples are exploited to illustrate the effectiveness of the proposed design schemes.
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Metadata
Title
Exponential synchronization of two totally different chaotic systems based on a unified model
Authors
Meiqin Liu
Haiyang Chen
Senlin Zhang
Zhen Fan
Publication date
01-12-2014
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 7-8/2014
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1670-5

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