SOM神经网络在独立学院招生决策中的应用

摘要

Self-Organizing Map(SOM) is a competitive unsupervised learning neural network method.The SOM neural network has been widely used in pattern classification and recognition, and topology invariance mapping, etc.This paper delves into the application of SOM in the clustering analysis of China's 31 provinces and constructs an enrollment decision model for independent colleges.First, it selects registration rate, first wish rate and per capital GDP in each province as the input mode of SOM;then, it uses SOM to cluster; finally, it obtains the features and level of student source by analyzing the clustering results.The experimental results show that SOM is feasible and effective to the cluster analysis of student sources; and it can avoid subjective factors and more rapidly and objectively get the clustering results.It provides a new reference basis for making a list of enrollment plan and an enrollment propaganda program for independent colleges, which has potential prospects in the field of enrollment for independent colleges.%自组织映射(SOM)是一种竞争型无指导学习的神经网络方法.SOM神经网络已广泛地应用于模式聚类、模式识剐、拓扑不变性映射等方面.本文利用SOM对中国31个省份进行聚类分析,建立独立学院招生决策模型.首先,选取各省份的报到率、第一志愿率和人均GDP等作为SOM神经网络的输入模式;然后,用SOM进行聚类;最后,对聚类结果进行分析得出各类的生源地特征和等级.实验结果表明,利用SOM对生源地进行聚类分析是可行的、有效的,可以避开人的主观因素,更迅速客观地得到聚类结果.它为独立学院编制招生计划和招生宣传方案提供了一种新的参考依据,在独立学院招生领域具有较好的应用前景.