Multiobjective synchronization of coupled systems

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 hybri...

Full description

Saved in:
Bibliographic Details
Published in:Chaos (Woodbury, N.Y.) N.Y.), 2011-06, Vol.21 (2), p.025114-025114-12
Main Authors: Tang, Yang, Wang, Zidong, Wong, W. K., Kurths, Jürgen, Fang, Jian-an
Format: Article
Language:English
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
cited_by cdi_FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673
cites cdi_FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673
container_end_page 025114-12
container_issue 2
container_start_page 025114
container_title Chaos (Woodbury, N.Y.)
container_volume 21
creator Tang, Yang
Wang, Zidong
Wong, W. K.
Kurths, Jürgen
Fang, Jian-an
description 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.
doi_str_mv 10.1063/1.3595701
format article
fullrecord <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_crossref_primary_10_1063_1_3595701</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>884423445</sourcerecordid><originalsourceid>FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673</originalsourceid><addsrcrecordid>eNp9kFtLwzAYQIMobk4f_AOyN1HozJdb2xdBhjeY-KLPoc0FO9qmNu1g_nqzi4rIhMAXkpNDOAidAp4AFvQKJpSnPMawh4aAkzSKRUL2V3vOIuAYD9CR93OMMRDKD9GAQBxWSoYInvqyK1w-N6orFmbsl7V6a11dfGThuB47O1aub0qjw5XvTOWP0YHNSm9OtnOEXu9uX6YP0ez5_nF6M4sUY6yLiFKpZSA0CEyFVZxTDhhoqnKSJ2CVjgXXQjMcQ8ZVQmmYhjCas0woEdMROt94m9a998Z3siq8MmWZ1cb1XiYJY4QyxgN5sSFV67xvjZVNW1RZu5SA5SqQBLkNFNizrbXPK6O_ya8iAbjeAF4V3brBbtvvdnLdLggudwkWrv15LBtt_4P__v0TvpOQ6A</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>884423445</pqid></control><display><type>article</type><title>Multiobjective synchronization of coupled systems</title><source>American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)</source><creator>Tang, Yang ; Wang, Zidong ; Wong, W. K. ; Kurths, Jürgen ; Fang, Jian-an</creator><creatorcontrib>Tang, Yang ; Wang, Zidong ; Wong, W. K. ; Kurths, Jürgen ; Fang, Jian-an</creatorcontrib><description>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><identifier>ISSN: 1054-1500</identifier><identifier>EISSN: 1089-7682</identifier><identifier>DOI: 10.1063/1.3595701</identifier><identifier>PMID: 21721792</identifier><identifier>CODEN: CHAOEH</identifier><language>eng</language><publisher>United States: American Institute of Physics</publisher><ispartof>Chaos (Woodbury, N.Y.), 2011-06, Vol.21 (2), p.025114-025114-12</ispartof><rights>American Institute of Physics</rights><rights>2011 American Institute of Physics</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673</citedby><cites>FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,777,781,27905,27906</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/21721792$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Tang, Yang</creatorcontrib><creatorcontrib>Wang, Zidong</creatorcontrib><creatorcontrib>Wong, W. K.</creatorcontrib><creatorcontrib>Kurths, Jürgen</creatorcontrib><creatorcontrib>Fang, Jian-an</creatorcontrib><title>Multiobjective synchronization of coupled systems</title><title>Chaos (Woodbury, N.Y.)</title><addtitle>Chaos</addtitle><description>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><issn>1054-1500</issn><issn>1089-7682</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2011</creationdate><recordtype>article</recordtype><recordid>eNp9kFtLwzAYQIMobk4f_AOyN1HozJdb2xdBhjeY-KLPoc0FO9qmNu1g_nqzi4rIhMAXkpNDOAidAp4AFvQKJpSnPMawh4aAkzSKRUL2V3vOIuAYD9CR93OMMRDKD9GAQBxWSoYInvqyK1w-N6orFmbsl7V6a11dfGThuB47O1aub0qjw5XvTOWP0YHNSm9OtnOEXu9uX6YP0ez5_nF6M4sUY6yLiFKpZSA0CEyFVZxTDhhoqnKSJ2CVjgXXQjMcQ8ZVQmmYhjCas0woEdMROt94m9a998Z3siq8MmWZ1cb1XiYJY4QyxgN5sSFV67xvjZVNW1RZu5SA5SqQBLkNFNizrbXPK6O_ya8iAbjeAF4V3brBbtvvdnLdLggudwkWrv15LBtt_4P__v0TvpOQ6A</recordid><startdate>20110601</startdate><enddate>20110601</enddate><creator>Tang, Yang</creator><creator>Wang, Zidong</creator><creator>Wong, W. K.</creator><creator>Kurths, Jürgen</creator><creator>Fang, Jian-an</creator><general>American Institute of Physics</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope></search><sort><creationdate>20110601</creationdate><title>Multiobjective synchronization of coupled systems</title><author>Tang, Yang ; Wang, Zidong ; Wong, W. K. ; Kurths, Jürgen ; Fang, Jian-an</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2011</creationdate><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Tang, Yang</creatorcontrib><creatorcontrib>Wang, Zidong</creatorcontrib><creatorcontrib>Wong, W. K.</creatorcontrib><creatorcontrib>Kurths, Jürgen</creatorcontrib><creatorcontrib>Fang, Jian-an</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><jtitle>Chaos (Woodbury, N.Y.)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Tang, Yang</au><au>Wang, Zidong</au><au>Wong, W. K.</au><au>Kurths, Jürgen</au><au>Fang, Jian-an</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Multiobjective synchronization of coupled systems</atitle><jtitle>Chaos (Woodbury, N.Y.)</jtitle><addtitle>Chaos</addtitle><date>2011-06-01</date><risdate>2011</risdate><volume>21</volume><issue>2</issue><spage>025114</spage><epage>025114-12</epage><pages>025114-025114-12</pages><issn>1054-1500</issn><eissn>1089-7682</eissn><coden>CHAOEH</coden><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.</abstract><cop>United States</cop><pub>American Institute of Physics</pub><pmid>21721792</pmid><doi>10.1063/1.3595701</doi><tpages>12</tpages><oa>free_for_read</oa></addata></record>
fulltext fulltext
identifier ISSN: 1054-1500
ispartof Chaos (Woodbury, N.Y.), 2011-06, Vol.21 (2), p.025114-025114-12
issn 1054-1500
1089-7682
language eng
recordid cdi_crossref_primary_10_1063_1_3595701
source American Institute of Physics:Jisc Collections:Transitional Journals Agreement 2021-23 (Reading list)
title Multiobjective synchronization of coupled systems
url http://sfxeu10.hosted.exlibrisgroup.com/loughborough?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-01-18T18%3A17%3A59IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-proquest_cross&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Multiobjective%20synchronization%20of%20coupled%20systems&rft.jtitle=Chaos%20(Woodbury,%20N.Y.)&rft.au=Tang,%20Yang&rft.date=2011-06-01&rft.volume=21&rft.issue=2&rft.spage=025114&rft.epage=025114-12&rft.pages=025114-025114-12&rft.issn=1054-1500&rft.eissn=1089-7682&rft.coden=CHAOEH&rft_id=info:doi/10.1063/1.3595701&rft_dat=%3Cproquest_cross%3E884423445%3C/proquest_cross%3E%3Cgrp_id%3Ecdi_FETCH-LOGICAL-c444t-2cc9f416d16036fc553510139cb2b81fcd765d6d4071a5c83371ae243b4a6c673%3C/grp_id%3E%3Coa%3E%3C/oa%3E%3Curl%3E%3C/url%3E&rft_id=info:oai/&rft_pqid=884423445&rft_id=info:pmid/21721792&rfr_iscdi=true