Identification Of Discrete Event Systems Using The Compound Recurrent Neural Network: Extracting DEVS From Trained Network  

Oleh:

Choi, Si Jong ; Kim, Tag Gon

Jenis: Article from Journal - ilmiah internasional
Dalam koleksi: Simulation vol. 78 no. 2 (Feb. 2002), page 90-104.
Topik: Discrete event system identification; DEVS formalism; neural network; model extraction; model minimization
Fulltext: 90.pdf (603.87KB)

Isi artikel

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.

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