Some Applications of Hybrid Fuzzy Modeling

Author

Valdés, Mercedes ; Botia, Juan A ; Gómez-Skarmeta, Antonio F.

Author_Institution

Dept. de Ingenieria de la Informacion y las Comunicaciones, Univ. de Murcia

fYear

2005

fDate

25-25 May 2005

Firstpage

607

Lastpage

612

Abstract

Fuzzy modeling is an effective approach for system identification. It is based on fuzzy sets and logic and describes the system behaviour by means of fuzzy IF-THEN rules. In its turn, data driven fuzzy modeling (DDFM) extracts these models from a set of input-output observations about the system. Three main stages compose DDFM: rules number identification, rules generation and parameter optimization. One way to carry out a DDFM process is by means of a combination of techniques, each one solving one of the DDFM phases. In this paper, the authors applied hybridizations of clustering algorithms and neural networks (NN) in order to solve several regression problems from different domains showing up the suitability and success of hybridization in DDFM

Keywords

fuzzy logic; fuzzy neural nets; fuzzy set theory; identification; knowledge based systems; modelling; optimisation; pattern clustering; IF-THEN rules; clustering algorithms; data driven fuzzy modeling; fuzzy logic; fuzzy sets; hybrid fuzzy modeling; neural networks; parameter optimization; rule number identification; rules generation; system identification; Clustering algorithms; Clustering methods; Data mining; Fuzzy logic; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Neural networks; Sensor fusion; Solar radiation;

fLanguage

English

Publisher

ieee

Conference_Titel

Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on

Conference_Location

Reno, NV

Print_ISBN

0-7803-9159-4

Type

conf

DOI

10.1109/FUZZY.2005.1452463

Filename

1452463