基于改进RBF网络的多变量序列建模和预测

Modeling and Prediction of Multivariate Series Based on an Improved RBF Network

针对复杂系统多变量序列预测研究中数据样本过多、信息冗余等问题,从学习样本选择和聚类中心优化两方面对径向基函数(RBF)网络进行改进.基于复杂系统多变量时间序列,首先采用一个线性相关函数和一个非线性相关函数分别计算多变量状态间的线性相关性和非线性相关性,确定一个包含系统有效信息的小数据集;然后基于小数据集,采用K均值聚类方法确定RBF网络的隐层聚类中心,并引入局部搜索过程,优化聚类中心结果;输入其它训练样本,确定网络权值.仿真结果表明,与常规RBF网络学习方法比较,在隐层节点数目相同情况下,改进的方法有效地确定了网络的聚类中心,达到更好的预测精度.

Aiming at some problems in multivariate series prediction of complex systems,such as data samples excess,information redundancy and so on,the radial basis function（RBF） network is improved from two aspects： the learning samples selection and the clustering centers optimization.Based on multivariate time series of complex systems,a linear correlation function and a nonlinear correlation function are used respectively to detect the linear correlations and the nonlinear correlations in the multivariate states firstly.A small data set which includes effective information of the system is defined.Then based on the small data set,K-means clustering algorithm is applied to adjusting hidden layer’s clustering centers of the RBF neural network.A local search procedure is introduced to optimize the clustering centers.Network weights are determined by inputting other training samples.Simulation results show that compared with the learning method based on conventional RBF network,the improved method determines the clustering centers of the network more effectively and gets better prediction accuracy when they have same numbers of hidden layer’s nodes.