文献类型：期刊文献

中文题名：SOM-T2 FLS在股市预测中的应用研究

英文题名：Application of SOM-T2 FLS in Stock Market Forecasting

作者：袁顺杰[1];程辉[1];叶贞成[1];程培鑫[1]

机构：[1]华东理工大学信息科学与工程学院自动化系,上海200237

年份：2020

卷号：56

期号：7

起止页码：130

中文期刊名：计算机工程与应用

外文期刊名：Computer Engineering and Applications

收录：CSTPCD;;北大核心:【北大核心2017】；CSCD:【CSCD_E2019_2020】；

基金：国家自然科学基金青年项目(No.61703163);上海市自然科学基金(No.16ZR1407300);中央高校基本科研业务费重点科研基地创新基金(No.22221817014)。

语种：中文

中文关键词：自组织特征映射;2型模糊逻辑系统;规则库;模型复杂度

外文关键词：self-organizing feature map;Type-2 Fuzzy Logic System(T2 FLS);rule base;model complexity

摘要：由于金融市场的复杂性和特殊性,现有预测算法的性能在牛市和熊市中呈现出较大差异,导致分类精度不高和抗风险能力不强,提出一种基于自组织特征映射-2型模糊逻辑系统(SOM-Type-2 Fuzzy Logic System,SOMT2 FLS)的分类算法。通过SOM网络将样本集分成两个不同子集,然后在每一个子集下分别学习T2 FLS分类器。在分类器学习过程中,提出将规则库的长度作为正则项,降低模型复杂度。利用中国证券市场的历史数据,验证了该算法较现有算法具有更好的预测效果和抗风险能力。
Due to the complexity and particularity of financial market, the performance of existing forecasting algorithms is quite different in bull and bear markets, which results in low classification accuracy and low risk resistance. A classification algorithm based on SOM-Type-2 Fuzzy Logic System(SOM-T2 FLS)is proposed. Firstly, the samples are divided into two different subsets by SOM. Then, the T2 FLS classifier is learned under each subset. During the learning of T2 FLS classifier, the length of rule base is proposed as a regular term to reduce the complexity of the model. Finally, using the historical data of Chinese stock market, it is verified that the algorithm has better classification performance and anti-risk ability than the existing algorithms.