Fault diagnosis based on genetic algorithm for optimization of EBF neural network

Abstract

Ellipsoidal basis function(EBF) can make the partition and limitary of input space. Compared with the Guassian function of radial basis function(RBF) neural network, the EBF can make the partition of input space more specific, which has the higher capability of pattern recognition. However, the neural network has a common problem of training the weight and threshold. The evolution of genetic algorithm(GA) can maximumly optimize the training time of neural network. In this paper, a new method based on GA-EBF neural network was proposed. The simulation experiment shows that the proposed method has a higher rate of fault diagnosis than that of RBF neural network.1