Modelling of a magneto-rheological damper by evolving radial basis function networks
- 作者：Haiping Du ; James Lam ; Nong Zhang
- 关键词：Magneto-rheological dampers ; Genetic algorithms ; Radial basis function networks
- 刊名：Engineering Applications of Artificial Intelligence
- 出版年：2006
- 期刊代码：51_09521976
- 类别：cp
- 出版时间：December 2006
- 卷：19
- 期：8
- 页码：869-881
- 文件大小：445 K
摘要
This paper presents an approach to approximate the forward and inverse dynamic behaviours of a magneto-rheological (MR) damper using evolving radial basis function (RBF) networks. Due to the highly nonlinear characteristics of MR dampers, modelling of MR dampers becomes a very important problem to their applications. In this paper, an alternative representation of the MR damper in terms of evolving RBF networks, which have a structure of four input neurons and one output neuron to emulate the forward and inverse dynamic behaviours of an MR damper, respectively, is developed by combining the genetic algorithms (GAs) to search for the network centres with other standard learning algorithms. Training and validating of the evolving RBF network models are achieved by using the data generated from the numerical simulation of the nonlinear differential equations proposed for the MR damper. It is shown by the validation tests that the evolving RBF networks can represent both forward and inverse dynamic behaviours of the MR damper satisfactorily.