Supervised learning in fuzzy systems: Algorithms and computational capabilities
- 30 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
Abstract
The author presents model structures for fuzzy systems and accompanies these model structures with learning algorithms. The emphasis is on basic principles of the design, operating characteristics, and adaptation of fuzzy systems. Several supervised learning algorithms for the adjustment of parameters are discussed. Results of simulations of function approximation and system identification demonstrate that the model structures and supervised learning algorithms suggested for fuzzy systems are practically feasible.Keywords
This publication has 5 references indexed in Scilit:
- Fuzzy systems are universal approximatorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Fuzzy systems as universal approximatorsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Learning representations by back-propagating errorsNature, 1986
- Application of fuzzy algorithms for control of simple dynamic plantProceedings of the Institution of Electrical Engineers, 1974
- Outline of a New Approach to the Analysis of Complex Systems and Decision ProcessesIEEE Transactions on Systems, Man, and Cybernetics, 1973