Stability of Fuzzy Infinite-state Automaton

Authors:
Qinge Wu;Tuo Wang;Yongxuan Huang;Jisheng Li
Affiliations:
School of Electronic and Information Engineering, Xi'an Jiao Tong University, Xi'an, Shaanxi, 710049, P.R. China;School of Electronic and Information Engineering, Xi'an Jiao Tong University, Xi'an, Shaanxi, 710049, P.R. China;School of Electronic and Information Engineering, Xi'an Jiao Tong University, Xi'an, Shaanxi, 710049, P.R. China;School of Electronic and Information Engineering, Xi'an Jiao Tong University, Xi'an, Shaanxi, 710049, P.R. China
Venue:
Proceedings of the 2006 conference on Advances in Intelligent IT: Active Media Technology 2006
Year:
2006

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Learning and extracting finite state automata with second-order recurrent neural networks

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Abstract

For better solving some complicated problems in fuzzy information processing, this paper presents a kind of new automaton--fuzzy infinite-state automaton. The extraction algorithm of fuzzy infinite-state automaton is given by using the neural networks. To the extracted fuzzy infinite-state automaton, its stability will be discussed. Finally, simulation is carried on and the simulation results show that the stability of fuzzy infinite-state automaton is achieved with extraction algorithm and training for weights what this paper provides. These problems to be solved will not only inject some new stronger powers into hierarchy of fuzzy automata, but also again increase a new tool for application of fuzzy signal processing. Finally, some problems of fuzzy automata and neural networks to be solved and development trends are discussed.