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Conference Paper: Agent swarm classification network ASCN

TitleAgent swarm classification network ASCN
Authors
Chow, C-KTsui, H-T
KeywordsMulti-Agent System
RBF neural network
Classifier
Issue Date2004
PublisherInstitute of Electrical and Electronics Engineers. The Journal's website is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9622
Citation
The 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), Hague, The Netherlands, 10-13 October 2004. In IEEE International Conference on Systems, Man, and Cybernetics. Conference Proceedings, 2004, v. 6, p. 5604-5608 How to Cite?
DOI: http://dx.doi.org/10.1109/ICSMC.2004.1401086
AbstractIn this paper we introduced a newly RBF Classification Network - "Agent Swarm Classification Network ASCN", which is trained by a Multi-agent systems (MAS) approach. MAS can be regarded as a swarm of independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. By treating each neuron as an agent, the weights of neurons can be determined through a set of pre-defined simple agent behavior. Three sets of experiments are examined to observe the effectiveness of the proposed method. © 2004 IEEE.
Persistent Identifierhttp://hdl.handle.net/10722/196651
ISBN
0-7803-8566-7
ISSN
1062-922X
2020 SCImago Journal Rankings: 0.168

 

DC FieldValueLanguage
dc.contributor.authorChow, C-K-
dc.contributor.authorTsui, H-T-
dc.date.accessioned2014-04-24T02:10:31Z-
dc.date.available2014-04-24T02:10:31Z-
dc.date.issued2004-
dc.identifier.citationThe 2004 IEEE International Conference on Systems, Man and Cybernetics (SMC 2004), Hague, The Netherlands, 10-13 October 2004. In IEEE International Conference on Systems, Man, and Cybernetics. Conference Proceedings, 2004, v. 6, p. 5604-5608-
dc.identifier.isbn0-7803-8566-7-
dc.identifier.issn1062-922X-
dc.identifier.urihttp://hdl.handle.net/10722/196651-
dc.description.abstractIn this paper we introduced a newly RBF Classification Network - "Agent Swarm Classification Network ASCN", which is trained by a Multi-agent systems (MAS) approach. MAS can be regarded as a swarm of independent software agents interact with each other to achieve common goals, complete concurrent distributed tasks under autonomous control. By treating each neuron as an agent, the weights of neurons can be determined through a set of pre-defined simple agent behavior. Three sets of experiments are examined to observe the effectiveness of the proposed method. © 2004 IEEE.-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers. The Journal's website is located at http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=9622-
dc.relation.ispartofIEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings-
dc.rights©2004 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.-
dc.subjectMulti-Agent System-
dc.subjectRBF neural network-
dc.subjectClassifier-
dc.titleAgent swarm classification network ASCN-
dc.typeConference_Paper-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1109/ICSMC.2004.1401086-
dc.identifier.scopuseid_2-s2.0-15744384843-
dc.identifier.volume6-
dc.identifier.spage5604-
dc.identifier.epage5608-
dc.publisher.placeUnited States-
dc.customcontrol.immutablesml 160603 amended-
dc.identifier.issnl1062-922X-

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