Identification of Non-Linear Systems, Based on Neural Networks, with Applications at Fuzzy Systems

The paper presents a short review how to use feedforward neural networks for non-linear system identification, with application at the neural implementation of a fuzzy system. In this application the inputoutput transfer characteristics of the fuzzy system are used to evaluate the accuracy of the identification results expressed for a neuro-fuzzy model. This method could be used for identification of the most general fuzzy systems, which are non-linear systems, being developed with all kind of fuzzyfication methods, rule bases, inference methods and defuzzification methods. Using this method, accurate neural models for a large class of fuzzy systems may be obtained. The neuro-fuzzy model preserves all the properties of the fuzzy systems: the values of the transfer gain and the sector property. The neuro-fuzzy model obtained by identification is useful in all applications of the fuzzy systems, for example in control. Good values of the quality neural identification criteria are obtained. The optimized neuro-fuzzy model is given by its structure, weights and biases, related to the most adequate training method. Key-Words: Neural networks, neural network training methods, fuzzy systems, empirical neural network training quality criteria, neuro-fuzzy systems.