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Conference Paper: Agent swarm classification network ASCN

TitleAgent swarm classification network ASCN
Authors
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?
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
ISSN

 

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.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsIEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings. Copyright © Institute of Electrical and Electronics Engineers.-
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-

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