Optimization of hierarchical neural fuzzy models

Author

Campello, Ricardo J G B ; Amaral, Wagner C.

Author_Institution

DCA, UNICAMP, Campinas, SP, Brazil

Volume

5

fYear

2000

fDate

2000

Firstpage

8

Abstract

Hierarchical fuzzy structures were introduced in previous work to deal with the dimensionality problem which is the main drawback to the application of neural networks and fuzzy models in the modeling and control of large-scale systems. In the present paper, the use of Radial Basis Function (RBF) networks connected in a hierarchical (cascade) fashion is investigated. The RBF networks are formulated as simplified fuzzy systems and the backpropagation equations for the optimization of the resulting hierarchical models are derived from this formulation. The optimization of the models using the conjugate gradient algorithm of Fletcher and Reeves is proposed and illustrated by means of a numerical example

Keywords

fuzzy neural nets; optimisation; radial basis function networks; Radial Basis Function; cascade; conjugate gradient; fuzzy models; hierarchical; large-scale systems; neural fuzzy models; neural networks; Control system synthesis; Electronic mail; Equations; Fuzzy control; Fuzzy neural networks; Fuzzy systems; Large-scale systems; Mathematical model; Neural networks; Radial basis function networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 2000. IJCNN 2000, Proceedings of the IEEE-INNS-ENNS International Joint Conference on

Conference_Location

Como

ISSN

1098-7576

Print_ISBN

0-7695-0619-4

Type

conf

DOI

10.1109/IJCNN.2000.861427

Filename

861427