期刊
IEEE TRANSACTIONS ON FUZZY SYSTEMS
卷 9, 期 4, 页码 516-524出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/91.940965
关键词
fuzzy classifier; genetic algorithm (GA); Iris data; rule base reduction; Takagi-Sugeno (T-S) fuzzy model; transparency and accuracy
In our previous work we showed that genetic algorithms (GAS) provide a powerful tool to increase the accuracy of fuzzy models for both systems modeling and classification. In addition to these results, we explore the GA to End redundancy in the fuzzy model for the purpose of model reduction. An aggregated similarity measure is applied to search for redundancy in the rule base description. As a result, we propose an iterative fuzzy identification technique starting with data-based fuzzy clustering with an overestimated number of local models. The GA is then applied to find redundancy among the local models with a criterion based on maximal accuracy and maximal set similarity. After the reduction steps, the GA is applied with another criterion searching for minimal set similarity and maximal accuracy. This results in an automatic identification scheme with fuzzy clustering, rule base simplification and constrained genetic optimization with low-human intervention. The proposed modeling approach is then demonstrated for a system identification and a classification problem. Results are compared to other approaches in the literature. Attractive models with respect to compactness, transparency and accuracy, are the result of this symbiosis.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据