摘要
无监督的自组织映射(SOM)神经网络是用于聚类的主要人工神经网络模型之一。在SOM网络的基础上改进了网络中的邻域函数,并将其用于对股票进行分析和选择,得到了令人满意的结果。为了提高解的精度,避免多个输入样本映射到同一输出节点还提出了禁忌映射的方法。数值模拟表明该模型对于上市公司的聚类结果令人满意,对于股民客观、准确地选出真正具有投资价值的股票具有指导意义。
Unsupervised self-organizing map network, also called SOM, is one of neural networks which are apphed to cluster. The neighborhood function is improved in SOM network and the improved SOM is used to analyze and select stock, and the results are satisfied. A tabu-mapping method is proposed to avoid that the same output node is mapped by more than one input. Numerical experiments show that the clustering results of the proposed model for the companies in the stock market are fairly good. Therefore this model might be beneficial for the investors to select stocks objectively and correctly and then get more profits.
出处
《计算机工程与设计》
CSCD
北大核心
2008年第9期2426-2428,共3页
Computer Engineering and Design
基金
国家自然科学基金项目(60673023)
关键词
SOM神经网络
动态竞争
聚类
禁忌映射
股票分析
SOM neural network
dynamic competition
clustering
tabu mapping
stock analysis
