A Fuzzy Perceptron as a Generic Model for Neuro{Fuzzy Approaches

This paper presents a fuzzy perceptron as a generic model of multilayer fuzzy neural networks, or neural fuzzy systems, respectively. This model is suggested to ease the comparision of diierent neuro{fuzzy approaches that are known from the literature. A fuzzy perceptron is not a fuzziication of a common neural network architecture, and it is not our intention to enhance neural learning algorithms by fuzzy methods. The idea of the fuzzy perceptron is to provide an architecture that can be initialized with prior knowledge, and that can be trained using neural learning methods. The training is carried out in such a way that the learning result is interpretable in the form of linguistic fuzzy if{then rules. Next to the advantage of having a generic model to compare neuro{fuzzy models, the fuzzy perceptron can be specialized e.g. for data analysis and control tasks.