基于SOM的福建省城市可持续发展水平分类

基于人工神经网络(ANN)中自组织特征映射网络(SOM)的聚类功能,利用主成分分析法(PCA)提取城市社会、经济发展的多项指标,应用MATLAB 6.1软件的神经网络工具箱对福建省23个城市的可持续发展水平进行分类判定,得出分为6类的最终结果与实际情况基本相符;指出SOM网络可以避免传统聚类方法的不足,凭借其强大的学习功能,可较好地应用于城市发展的相关研究。

The classkfication on level of city sustainable developmemt in Fujian Province based on SOM

Based on the clustering function of self-organizing feature map(SOM)in artificial neural networks(ANN),the authors select some factors on integrated economic strength,people living standard and present environment condition in Fujian cities to classify the level of continuable development by using the neural network toolbox in MATLAB 6.1. And the principle component analysis(PCA)is used in factors'selection. Finally, 23 cities are classified into 6 groups. Above that,this paper analyses the result of classification and points out that SOM can avoid some insurmountable shortcoming in the traditional clustering methods. Its strong learning function,excellent self-organizing feature,self-adapting feature and robustness can work effectively on the study about cities'development.