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

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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.
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Issue Date
2010-09
Language
English
Article Type
Article
Keywords

LEVENBERG-MARQUARDT ALGORITHM; SYSTEMS; TIME; INFERENCE; MACHINES; NETS

Citation

IEEE TRANSACTIONS ON NEURAL NETWORKS, v.21, pp.1408 - 1421

ISSN
1045-9227
DOI
10.1109/TNN.2010.2059040
URI
http://hdl.handle.net/10203/100110
Appears in Collection
EE-Journal Papers(저널논문)
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