RBF Neural Networks and Its Application in the Simulation of a Random Vibration System

Radial basis function neural network (RBF-NN) is applied to simulate a random vibration system in this paper. First, an overview of random vibration system is introduced, and the dynamic model of the system and the spectral density of external excitation are presented. Then, a radial basis function neural network is proposed to predict the random vibration based on a biological neuron system. Radial basis Gaussian function and back-propagation learning algorithm are employed to train the proposed NN. The back-propagation algorithm updates the weights and thresholds of the RBF-NN are deduced in detail. At last, the RBF-NN is trained and used to predict the acceleration of random vibration system in different conditions. The simulation results show the advantages of radial basis Gaussian network in fast convergence of the results of different approaches, and the proposed RBF-NN can be used to simulate a random vibration system.