Abstract
A method for the systematic design of a fuzzy model is developed for the control of complex systems. The proposed fuzzy controller design is based on a maximum entropy self-organizing net (MESON) and the cell state mapping approach. For fuzzy model identification, we present an approach to constructing a self-organizing fuzzy identifier. The proposed identifier is built on a neuro-fuzzy system consisting of a maximum entropy self-organizing net and a radial basis function network. We develop the corresponding self-organizing algorithms. To design a fuzzy controller, the proposed method combines the concept of cell state mapping with the synthesis techniques of MESON used in the fuzzy model identification.
| 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 | 45-50 |
| Number of pages | 6 |
| 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 |
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| City | Syracuse, NY, USA |
| Period | 9/21/97 → 9/24/97 |
ASJC Scopus subject areas
- General Computer Science
- Media Technology