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 |
Issue Date: | Jun-2011 | Source: | Chaos, 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. | Keywords: | Cellular biophysics Chaos Complex networks Learning (artificial intelligence) Neural nets Sychronisation |
Publisher: | American Institute of Physics | Journal: | Chaos | ISSN: | 1054-1500 | EISSN: | 1089-7682 | 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 |
| Appears in Collections: | Journal/Magazine Article |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| Tang_Multiobjective_Synchronization_Coupled.pdf | 3.1 MB | Adobe PDF | View/Open |
Access
View full-text via PolyU eLinks 
Page views
143
Last Week
2
2
Last month
Citations as of May 28, 2023
Downloads
182
Citations as of May 28, 2023
SCOPUSTM
Citations
52
Last Week
0
0
Last month
0
0
Citations as of Jun 2, 2023
WEB OF SCIENCETM
Citations
44
Last Week
0
0
Last month
0
0
Citations as of Jun 1, 2023
Google ScholarTM
Check
Altmetric
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.