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

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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.
Publisher
SAGE PUBLICATIONS LTD
Issue Date
2002-02
Language
English
Article Type
Article
Keywords

TIME

Citation

SIMULATION-TRANSACTIONS OF THE SOCIETY FOR MODELING AND SIMULATION INTERNATIONAL, v.78, no.2, pp.90 - 104

ISSN
0037-5497
DOI
10.1177/0037549702078002210
URI
http://hdl.handle.net/10203/83561
Appears in Collection
EE-Journal Papers(저널논문)
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