Title :
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
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;
Conference_Titel :
Neural Networks, 1993., IEEE International Conference on
Conference_Location :
San Francisco, CA
Print_ISBN :
0-7803-0999-5
DOI :
10.1109/ICNN.1993.298698
