Neural network models for identification and realization of a class of discrete event systems
Yasuaki Kuroe, Yoshihiro Mori · 2007
This paper presents neural network models for identification and realization of a class of discrete event systems (DESs). We consider a class of DESs which is modeled by using finite state automata. Two neural network models are presented: one is a class of recurrent neural networks and the other is a class of recurrent high-order neural networks. The models are capable of representing the DESs with the network size being smaller than the existing models. We also discuss identification and realization methods of the DESs from a given set of input and output data by training the neural networks. Comparisons are made among the models in terms of abilities of identification and realization of the DESs.