An Optimization Method for the Initial Parameters Selection of Fuzzy Cerebellar Model Neural Networks in Parametric Fault Diagnosis

Abstract

When the initial parameters of the fuzzy cerebellar model neural network (FCMNN) are not properly selected, there is a great possibility that the error function converges to the local minimum region or diverges under the gradient descent back propagation (BP) algorithm, which will affect the classification ability of FCMNN. Aiming at this problem, GA is used to optimize the initial center positions and width of the activation function and weight of FCMNN. After obtaining the optimal initial parameters, the internal parameters of FCMNN can approach the convergence value of minimum error more quickly and accurately through further training of the network, so as to obtain better network learning performance. The introduction of GA to optimize the initial value of FCMNN can effectively reduce the blindness and time cost of manual selection of initial parameters, and further improve the intelligence of neural network diagnosers. The simulation and experimental results show that the classification ability of the GA-FCMNN and GA can effectively find the optimal combination in the set data domain.