Referenced Items are not available for this document.
Based on previous work on encoding deterministic finite-state automata (DFA) 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 (FFA). 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
Published in:
Neural Networks, 1996., IEEE International Conference on
(Volume:2
)
Date of Conference: 3-6 Jun 1996
- Page(s):
- 1023 - 1027 vol.2
- Meeting Date :
- 03 Jun 1996-06 Jun 1996
- Print ISBN:
- 0-7803-3210-5
- INSPEC Accession Number:
- 5385870
- Conference Location :
- Washington, DC
- Digital Object Identifier :
- 10.1109/ICNN.1996.549038
- Product Type:
- Conference Publications
About IEEE Xplore | Contact | Help | Terms of Use | Nondiscrimination Policy | Site Map | Privacy & Opting Out of Cookies
A not-for-profit organization, IEEE is the world's largest professional association for the advancement of technology.
© Copyright 2013 IEEE - All rights reserved. Use of this web site signifies your agreement to the terms and conditions.
