On the optimization of fuzzy systems using bio-inspired strategies

Author

De Oliveira, J. Valente

Author_Institution

Dept. of Math. & Comput. Sci., UBI, Covilha, Portugal

Volume

2

fYear

1998

fDate

4-9 May 1998

Firstpage

1229

Abstract

The optimization of fuzzy systems using bio-inspired strategies, such as neural network learning rules or evolutionary optimization techniques, is becoming more and more popular. In general, fuzzy systems optimized in such a way cannot provide a linguistic interpretation, preventing us from using one of their most interesting and useful features. This work addresses this difficulty and present a design methodology to overcome it. A set of properties that obviate the subjective task of interpreting linguistically fuzzy systems is provided. These properties are translated in terms of nonlinear constraints that are coded within a given optimization scheme, such as backpropagation. Illustrative numerical examples are also included

Keywords

backpropagation; computational linguistics; fuzzy set theory; fuzzy systems; genetic algorithms; neural nets; backpropagation; evolutionary optimization; fuzzy systems; genetic algorithm; learning rules; linguistic modelling; membership functions; neural network; optimization; semantics; Biological control systems; Biological system modeling; Computer science; Constraint optimization; Design methodology; Fuzzy sets; Fuzzy systems; Genetic algorithms; Mathematics; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence., The 1998 IEEE International Conference on

Conference_Location

Anchorage, AK

ISSN

1098-7584

Print_ISBN

0-7803-4863-X

Type

conf

DOI

10.1109/FUZZY.1998.686294

Filename

686294