Title :
A genetic-based method applied in fuzzy modeling
Author :
Fagarasan, Florin
Author_Institution :
Dept. of Fuzzy Syst., Inst. of Microtechnol., Bucharest, Romania
Abstract :
The identification of a fuzzy system model consists of two major phases: structure identification and parameter identification. The aim of the paper is to determine the main aspects involved in developing a flexible method able to learn and optimize both the structure and the parameters of a fuzzy inference system (FIS) with applications in fuzzy modeling. We propose a special kind of GA with variable length genotypes. We tried to avoid the difficult problem of designing a recombination operator for parents of different sizes because in the natural environment we usually cannot find a correspondence for it
Keywords :
fuzzy set theory; fuzzy systems; genetic algorithms; inference mechanisms; learning (artificial intelligence); learning systems; modelling; parameter estimation; fuzzy inference system; fuzzy modeling; fuzzy system model; genetic-based method; identification; learning; natural environment; optimization; parameter identification; recombination operator; structure identification; variable length genotypes; Computer networks; Fuzzy neural networks; Fuzzy reasoning; Fuzzy set theory; Fuzzy sets; Fuzzy systems; Intelligent networks; Neural networks; Optimization methods; Parameter estimation;
Conference_Titel :
Evolutionary Computation, 1996., Proceedings of IEEE International Conference on
Conference_Location :
Nagoya
Print_ISBN :
0-7803-2902-3
DOI :
10.1109/ICEC.1996.542370
