Representation of fuzzy finite state automata in continuous recurrent neural networks

Christian W. Omlin, Karvel K. Thornber, C. Lee Giles

Research output: Chapter in Book/Report/Conference proceedingConference contribution

9 Scopus citations

Abstract

Based on previous work on encoding deterministic finite-state automata (DFAs) in discrete-time, second-order recurrent neural networks with sigmoidal discriminant functions, we propose an algorithm that constructs an augmented recurrent neural network that encodes fuzzy finite-state automata (FFAs). Given an arbitrary FFA, we apply an algorithm which transforms the FFA into an equivalent deterministic acceptor which computes the fuzzy string membership function. The neural network can be constructed such that it recognizes strings of fuzzy regular languages with arbitrary accuracy.

Original languageEnglish (US)
Title of host publicationIEEE International Conference on Neural Networks - Conference Proceedings
PublisherIEEE
Pages1023-1027
Number of pages5
Volume2
StatePublished - 1996
EventProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA
Duration: Jun 3 1996Jun 6 1996

Other

OtherProceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4)
CityWashington, DC, USA
Period6/3/966/6/96

All Science Journal Classification (ASJC) codes

  • Software

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  • Cite this

    Omlin, C. W., Thornber, K. K., & Giles, C. L. (1996). Representation of fuzzy finite state automata in continuous recurrent neural networks. In IEEE International Conference on Neural Networks - Conference Proceedings (Vol. 2, pp. 1023-1027). IEEE.