A Revised Clustering Algorithm Based RBF Neural Network Approach for Modeling of an Electro-hydraulic Servo System

摘要：

The paper presents an approach to model the electrohydraulic system of a certain explosive mine sweeping device using the Radial Basis Function (RBF) neural network. In order to obtain accurate and simple RBF neural network, a revised clustering method is used to train the hidden node centers of the neural network, in which the subtractive clustering(SC) algorithm was used to determine the initial centers and the fuzzy C - Means(FCM) clustering algorithm to further determined the centers data set. The spread factors and the weights of the neural network are calculated by the modified recursive least squares (MRLS) algorithm for relieving computational burden. The proposed algorithm is verified by its application to the modeling of an electro-hydraulic system, simulation and experiment results clearly indicate the obtained RBF network can model the electrohydraulic system satisfactorily and comparison results also show that the proposed algorithm performs better than the other methods.

关键词： explosive sweeping mine device clustering algorithm recursive least squares algorithm radial basis function neural network modeling

作者: Chen Jilin Qian Jin Jong Zhongzhi Hou Yuanlong

作者单位: Nanjing University of Science and Technology, Nanjing, China, 210094

会议类型: 国际会议

会议名称: 2011 International Conference on Mechatronics and Applied Mechanics(2011年机电一体化与应用力学国际会议 ICMAM 2011)

会议地点: 香港

会议语种:英文

页码: 1595-1600

在线出版日期: 2011-12-27（万方平台首次上网日期，不代表论文的发表时间）