Support vector learning mechanism for fuzzy rule-based modeling: a new approach
Support vector learning mechanism for fuzzy rule-based modeling: a new approach
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DOI：
10.1109/tfuzz.2003.817839
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发表时间：
2004-02
期刊：
影响因子：
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通讯作者：
J. Chiang;Pei-Yi Hao
中科院分区：
文献类型：
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作者：
J. Chiang;Pei-Yi Hao
本文描述了一种基于支持向量机的模糊建模框架，该框架明确地描述了模糊推理过程中的表示。支持向量学习机制提供了一种从训练数据集中提取支持向量以生成模糊IF-THEN规则的体系结构，以及一种用核函数描述模糊系统的方法。因此，其固有的优点是模型不需要事先确定规则的个数，整个模糊推理系统可以表示为模糊基函数的级数展开。利用四个数据集，将该方法的性能与其他基于模糊规则的建模方法进行了比较。
This paper describes a fuzzy modeling framework based on support vector machine, a rule-based framework that explicitly characterizes the representation in fuzzy inference procedure. The support vector learning mechanism provides an architecture to extract support vectors for generating fuzzy IF-THEN rules from the training data set, and a method to describe the fuzzy system in terms of kernel functions. Thus, it has the inherent advantage that the model does not have to determine the number of rules in advance, and the overall fuzzy inference system can be represented as series expansion of fuzzy basis functions. The performance of the proposed approach is compared to other fuzzy rule-based modeling methods using four data sets.