Variable cluster analysis method for building neural network model^①

来源期刊：中南大学学报(英文版)2004年第2期

论文作者：王海东 刘元东

文章页码：220 - 224

Key words：variable cluster; neural network; information theory; cluster tree

Abstract: To address the problems that input variables should be reduced as much as possible and explain output vari-ables fully in building neural network model of complicated system, a variable selection method based on cluster analysis was investigated. Similarity coefficient which describes the mutual relation of variables was defined. The methods of the highest contribution rate, part replacing whole and variable replacement are put forwarded and deduced by information theory. The software of the neural network based on cluster analysis, which can provide many kinds of methods for defin-ing variable similarity coefficient, clustering system variable and evaluating variable cluster, was developed and applied to build neural network forecast model of cement clinker quality. The results show that all the network scale, training time and prediction accuracy are perfect. The practical application demonstrates that the method of selecting variables for neural network is feasible and effective.