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

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2 Author(s)
Nauck, D. ; Dept. of Comput. Sci., Tech. Braunschweig Univ., Germany ; Kruse, R.

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

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Neural Networks, 1993., IEEE International Conference on

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