A method for designing flexible neuro-fuzzy systems

Authors:
Krzysztof Cpalka
Affiliations:
Department of Computer Engineering, Czestochowa University of Technology, Poland
Venue:
ICAISC'06 Proceedings of the 8th international conference on Artificial Intelligence and Soft Computing
Year:
2006

Citing 9
Cited 0

On fuzzy implication operators

Fuzzy Sets and Systems
Essentials of fuzzy modeling and control

Essentials of fuzzy modeling and control
Neuro-fuzzy systems for function approximation

Fuzzy Sets and Systems - Special issue on analytical and structural considerations in fuzzy modeling
Computational intelligence systems and applications: neuro-fuzzy and fuzzy neural synergisms

Computational intelligence systems and applications: neuro-fuzzy and fuzzy neural synergisms
Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)

Flexible Neuro-fuzzy Systems: Structures, Learning and Performance Evaluation (Kluwer International Series in Engineering and Computer Science)
Neuro-fuzzy structures for pattern classification

ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Self-adaptive neuro-fuzzy inference systems for classification applications

IEEE Transactions on Fuzzy Systems
Designing and learning of adjustable quasi-triangular norms with applications to neuro-fuzzy systems

IEEE Transactions on Fuzzy Systems
Flexible neuro-fuzzy systems

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.00

Visualization

Abstract

In the paper we develop a new method for designing and reduction of flexible neuro-fuzzy systems. The method allows to reduce number of discretization points in the defuzzifier, number of rules, number of inputs, and number of antecedents. The performance of our approach is illustrated on a typical benchmark.