A fuzzy neural network learning fuzzy control rules and membership functions by fuzzy error backpropagation
Detlef D. Nauck, Rudolf Kruse · 2002
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.>