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Identification of Finite State Automata With a Class of Recurrent Neural Networks

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4 Author(s)
Sung Hwan Won ; Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Daejeon, South Korea ; Iickho Song ; Sun Young Lee ; Cheol Hoon Park

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.

Published in:

Neural Networks, IEEE Transactions on  (Volume:21 ,  Issue: 9 )

Date of Publication:

Sept. 2010

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