Identification of discrete event systems using the compound recurrent neural network: Extracting DEVS from trained network

Cited 3 time in webofscience Cited 4 time in scopus
  • Hit : 402
  • Download : 0
DC FieldValueLanguage
dc.contributor.authorChoi, SJko
dc.contributor.authorKim, Tag-Gonko
dc.date.accessioned2013-03-04T18:07:20Z-
dc.date.available2013-03-04T18:07:20Z-
dc.date.created2012-02-06-
dc.date.created2012-02-06-
dc.date.issued2002-02-
dc.identifier.citationSIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, v.78, no.2, pp.90 - 104-
dc.identifier.issn0037-5497-
dc.identifier.urihttp://hdl.handle.net/10203/83561-
dc.description.abstractThe authors consider identifying an unknown discrete event system (DES) as recognition of characteristic functions of a discrete event systems specification (DEVS) model that validly represents the system. Such identification consists of two major steps: behavior learning using a specially designed neural network and extraction of a DEVS model from the learned neural network. This paper presents a method for extracting a DEVS model from one such neural network called CRNN (compound recurrent neural network), which is trained using observed input/output events of an unknown DES. The DES to be identified is restricted to a subclass of DES in which any unknown state can be determined by a finite number of input/output sequences. Identification experiments were performed with three types of unknown DESs, the result of which verified the validity of the proposed model extraction method.-
dc.languageEnglish-
dc.publisherSAGE PUBLICATIONS LTD-
dc.subjectTIME-
dc.titleIdentification of discrete event systems using the compound recurrent neural network: Extracting DEVS from trained network-
dc.typeArticle-
dc.identifier.wosid000177199200003-
dc.identifier.scopusid2-s2.0-0036478741-
dc.type.rimsART-
dc.citation.volume78-
dc.citation.issue2-
dc.citation.beginningpage90-
dc.citation.endingpage104-
dc.citation.publicationnameSIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL-
dc.identifier.doi10.1177/0037549702078002210-
dc.contributor.localauthorKim, Tag-Gon-
dc.contributor.nonIdAuthorChoi, SJ-
dc.type.journalArticleArticle-
dc.subject.keywordAuthordiscrete event system identification-
dc.subject.keywordAuthorDEVS formalism-
dc.subject.keywordAuthorneural network-
dc.subject.keywordAuthormodel extraction-
dc.subject.keywordAuthormodel minimization-
dc.subject.keywordPlusTIME-
Appears in Collection
EE-Journal Papers(저널논문)
Files in This Item
There are no files associated with this item.
This item is cited by other documents in WoS
⊙ Detail Information in WoSⓡ Click to see webofscience_button
⊙ Cited 3 items in WoS Click to see citing articles in records_button

qr_code

  • mendeley

    citeulike


rss_1.0 rss_2.0 atom_1.0