A fault identification approach for analog circuits using fuzzy neural network mixed with genetic algorithms

A fault identification approach for nonlinear analogue systems is presented. A fuzzy neural network is developed based on the improving fuzzy weighted reasoning method. The training of network weights and optimization of membership functions are conducted employing genetic algorithms. Fuzzy rules can be realized through the refresh of the weights of the neural network. The availability of the method is examined by simulated test examples.