A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation

Author

Nauck, Detlef ; Kruse, Rudolf

Author_Institution

Dept. of Comput. Sci., Tech. Braunschweig Univ., Germany

fYear

1993

fDate

1993

Firstpage

1022

Abstract

A kind of neural network architecture designed for control tasks is presented. It is called the fuzzy neural network. The structure of the network can be interpreted in terms of a fuzzy controller. It has a three-layered architecture and uses fuzzy sets as its weights. The fuzzy error backpropagation algorithm, a special learning algorithm inspired by the standard BP-procedure for multivariable neural networks, is able to learn the fuzzy sets. The extended version that is presented is also able to learn fuzzy-if-then rules by reducing the number of nodes in the hidden layer of the network. The network does not learn from examples, but by evaluating a special fuzzy error measure

Keywords

backpropagation; feedforward neural nets; fuzzy control; fuzzy set theory; fuzzy control rules; fuzzy error backpropagation; fuzzy neural network; fuzzy set theory; learning; membership functions; multivariable neural networks; three-layered architecture; Backpropagation algorithms; Computer errors; Control systems; Error correction; Fuzzy control; Fuzzy neural networks; Fuzzy sets; Fuzzy systems; Multi-layer neural network; Neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Neural Networks, 1993., IEEE International Conference on

Conference_Location

San Francisco, CA

Print_ISBN

0-7803-0999-5

Type

conf

DOI

10.1109/ICNN.1993.298698

Filename

298698