Abstract
In this paper we introduce a new architecture called recurrent neuro-fuzzy (RNF) system which enhances the modeling capabilities of fuzzy systems with the dynamic behavior of recurrent neural networks (RNN). In a general sense, the architecture of RNF is similar to other adaptive neuro-fuzzy systems. It has a rule-base, a database, an inference engine, and a learning mechanism. In this paper we will emphasize those portions which are different that other approaches, specifically, the construction and operation of recurrent rules and the learning mechanism which is used in determination and adaptation of system parameters. The fundamental concepts of the RNF system are demonstrated using a two-link robot example.
| Original language | English (US) |
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| Title of host publication | Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS |
| Publisher | IEEE Computer Society |
| Pages | 362-366 |
| Number of pages | 5 |
| State | Published - 1997 |
| Event | Proceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'97 - Syracuse, NY, USA Duration: Sep 21 1997 → Sep 24 1997 |
Other
| Other | Proceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'97 |
|---|---|
| City | Syracuse, NY, USA |
| Period | 9/21/97 → 9/24/97 |
ASJC Scopus subject areas
- General Computer Science
- Media Technology