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Support Vector Learning Mechanism for Fuzzy Rule-Based Modeling: A New Approach

2004; Institute of Electrical and Electronics Engineers; Volume: 12; Issue: 1 Linguagem: Inglês

10.1109/tfuzz.2003.817839

1941-0034

Jung-Hsien Chiang, Pei Yi Hao,

Time Series Analysis and Forecasting

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.