Modelling of Electrohydraulic System Using RBF Neural Networks and Genetic Algorithm

Abstract

This paper presents an approach to model the nonlinear dynamic behaviors of the Automatic Depth Control Electrohydraulic System (ADCES) of a certain mine-sweeping weapon using Radial Basis Function (RBF) neural networks. In order to obtain accurate RBF neural networks efficiently, a hybrid learning algorithm is proposed to train the neural networks, in which centers of neural networks are optimized by genetic algorithm, and widths and centers of neural networks are calculated by linear algebra methods. The proposed algorithm is applied to the modelling of the ADCES, and the results clearly indicate that the obtained RBF neural network can emulate the complex dynamic characteristics of the ADCES satisfactorily. The comparison results also show that the proposed algorithm performs better than the traditional clustering-based method.