自动化学报2012，Vol.38Issue(6)：999-1006,8.DOI:10.3724/SP.J.1004.2012.00999
基于互信息的分步式输入变量选择多元序列预测研究
Stepwise Input Variable Selection Based on Mutual Information for Multivariate Forecasting
摘要
Abstract
Input variable selection has many applications in the problem of multivariate time series. In this paper, a novel stepwise variable selection algorithm is proposed based on the ^-nearest mutual information estimation. Two steps are used to select the relevant variables and discard weak relevant variables. Meanwhile, the proposed variable selection algorithm is applied to the optimal structure design for radial basis function (RBF) neural networks. The hidden neurons are selected based on K-means clustering and the correlation between the hidden neuron weight and the output, to the purpose that the architecture and the performance of the networks can be improved. Simulation results of Friedman data show the validity of the proposed input variable selection algorithm. Simulation results of Gas furnace and Boston housing substantiate that the size of the improved RBF networks can be controlled on the basis of the model accuracy assured.关键词
互信息/变量选择/径向基函数网络/节点选择Key words
Mutual information, varaible selection, radial basis function (RBF) networks, neuron selection引用本文复制引用
韩敏,刘晓欣..基于互信息的分步式输入变量选择多元序列预测研究[J].自动化学报,2012,38(6):999-1006,8.基金项目
国家自然科学基金(61074096)资助 （61074096）
