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

Abstract:

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.

Key words: self-organizing feature map, Type-2 Fuzzy Logic System（T2 FLS）, rule base, model complexity