Identification of finite state automata with a class of recurrent neural networks

Authors:
Sung Hwan Won;Iickho Song;Sun Young Lee;Cheol Hoon Park
Affiliations:
Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea;Department of Electrical Engineering, Korea Advanced Institute of Science and Technology, Daejeon, Korea
Venue:
IEEE Transactions on Neural Networks
Year:
2010

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Abstract

A class of recurrent neural networks is proposed and proven to be capable of identifying any discrete-time dynamical system. The application of the proposed network is addressed in the encoding, identification, and extraction of finite state automata (FSAs). Simulation results show that the identification of FSAs using the proposed network, trained by the hybrid greedy simulated annealing with a modified cost function in the training stage, generally exhibits better performance than the conventional identification procedures.