Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/5270
Title: Multiobjective synchronization of coupled systems
Authors: Tang, Y
Wang, Z
Wong, WKC 
Kurths, J
Fang, JA
Keywords: Cellular biophysics
Chaos
Complex networks
Learning (artificial intelligence)
Neural nets
Sychronisation
Issue Date: Jun-2011
Publisher: American Institute of Physics
Source: Chaos: an interdisciplinary journal of nonlinear science, June 2011, v. 21, no. 2, 025114, p. 1-12 How to cite?
Journal: Chaos: an interdisciplinary journal of nonlinear science 
Abstract: In this paper, multiobjective synchronization of chaotic systems is investigated by especially simultaneously minimizing optimization of control cost and convergence speed. The coupling form and coupling strength are optimized by an improved multiobjective evolutionary approach that includes a hybrid chromosome representation. The hybrid encoding scheme combines binary representation with real number representation. The constraints on the coupling form are also considered by converting the multiobjective synchronization into a multiobjective constraint problem. In addition, the performances of the adaptive learning method and non-dominated sorting genetic algorithm-II as well as the effectiveness and contributions of the proposed approach are analyzed and validated through the Rössler system in a chaotic or hyperchaotic regime and delayed chaotic neural networks.
URI: http://hdl.handle.net/10397/5270
ISSN: 1054-1500 (print)
1089-7682 (online)
DOI: 10.1063/1.3595701
Rights: © 2011 American Institute of Physics. This article may be downloaded for personal use only. Any other use requires prior permission of the author and the American Institute of Physics. The following article appeared in Y. Tang et al., Chaos: an interdisciplinary journal of nonlinear science 21, 025114 (2011) and may be found at http://link.aip.org/link/?cha/21/025114
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