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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, Yang
Wang, Zidong
Wong, W. K. Calvin
Kurths, Jürgen
Fang, Jian-an
Subjects: Cellular biophysics
Complex networks
Learning (artificial intelligence)
Neural nets
Issue Date: Jun-2011
Publisher: American Institute of Physics
Citation: Chaos: an interdisciplinary journal of nonlinear science, June 2011, v. 21, no. 2, 025114, p. 1-12.
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.
Description: 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
Type: Journal/Magazine Article
URI: http://hdl.handle.net/10397/5270
ISSN: 1054-1500 (print)
1089-7682 (online)
Appears in Collections:ITC Journal/Magazine Articles

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