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On the Neural Defuzzification Methods.

Halgamuge, S.K.; Runkler, T.A.; Glesner, M.

In: Proc. 5th IEEE International Conference on Fuzzy Systems (FUZZ-IEEE'96), New Orleans, Louisiana, USA, Sep 8-11, 1996, pages 463-469. IEEE, 1996. ISBN 0-7803-3645-3.

Abstract: If representative real world or artifical data sets exist, neural networks can be trained to approximate different defuzzification methods - explicitly known standard methods like center of gravity, extended parameteric methods like customisable basic defuzzification distribution, and also black box defuzzification methods. From the neural network point of view this kind of defuzzification is a multidimensional function approximation problem. In non black box adaptive solutions the analysing capability of the trained network is significant to understand the specificty of the application.

Using random membership functions or a carefully selected variation of membership functions as training data, a black box defuzzification method with the lowest amplication known is achieved. The application of the trainable transparent defuzzification to a real world problem is presented. Since the neural defuzzification is an integral part of many neuro-fuzzy systems, such as example is also described.

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