DC Field | Value | Language |
---|---|---|
dc.contributor.author | Choi, SJ | ko |
dc.contributor.author | Kim, Tag-Gon | ko |
dc.date.accessioned | 2013-03-04T18:07:20Z | - |
dc.date.available | 2013-03-04T18:07:20Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2002-02 | - |
dc.identifier.citation | SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, v.78, no.2, pp.90 - 104 | - |
dc.identifier.issn | 0037-5497 | - |
dc.identifier.uri | http://hdl.handle.net/10203/83561 | - |
dc.description.abstract | The 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.language | English | - |
dc.publisher | SAGE PUBLICATIONS LTD | - |
dc.subject | TIME | - |
dc.title | Identification of discrete event systems using the compound recurrent neural network: Extracting DEVS from trained network | - |
dc.type | Article | - |
dc.identifier.wosid | 000177199200003 | - |
dc.identifier.scopusid | 2-s2.0-0036478741 | - |
dc.type.rims | ART | - |
dc.citation.volume | 78 | - |
dc.citation.issue | 2 | - |
dc.citation.beginningpage | 90 | - |
dc.citation.endingpage | 104 | - |
dc.citation.publicationname | SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL | - |
dc.identifier.doi | 10.1177/0037549702078002210 | - |
dc.contributor.localauthor | Kim, Tag-Gon | - |
dc.contributor.nonIdAuthor | Choi, SJ | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | discrete event system identification | - |
dc.subject.keywordAuthor | DEVS formalism | - |
dc.subject.keywordAuthor | neural network | - |
dc.subject.keywordAuthor | model extraction | - |
dc.subject.keywordAuthor | model minimization | - |
dc.subject.keywordPlus | TIME | - |
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.