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 language | English (US) |
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| Title of host publication | IEEE International Conference on Neural Networks - Conference Proceedings |
| Publisher | IEEE |
| Pages | 1023-1027 |
| Number of pages | 5 |
| Volume | 2 |
| State | Published - 1996 |
| Event | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) - Washington, DC, USA Duration: Jun 3 1996 → Jun 6 1996 |
Other
| Other | Proceedings of the 1996 IEEE International Conference on Neural Networks, ICNN. Part 1 (of 4) |
|---|---|
| City | Washington, DC, USA |
| Period | 6/3/96 → 6/6/96 |
All Science Journal Classification (ASJC) codes
- Software