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http://dx.doi.org/10.5391/JKIIS.2009.19.5.595

Design of Fuzzy System with Hierarchical Classifying Structures and its Application to Time Series Prediction  

Bang, Young-Keun (강원대학교 대학원 전기전자공학과)
Lee, Chul-Heui (강원대학교 IT특성화학부대학 전기전자공학부)
Publication Information
Journal of the Korean Institute of Intelligent Systems / v.19, no.5, 2009 , pp. 595-602 More about this Journal
Abstract
Fuzzy rules, which represent the behavior of their system, are sensitive to fuzzy clustering techniques. If the classification abilities of such clustering techniques are improved, their systems can work for the purpose more accurately because the capabilities of the fuzzy rules and parameters are enhanced by the clustering techniques. Thus, this paper proposes a new hierarchically structured clustering algorithm that can enhance the classification abilities. The proposed clustering technique consists of two clusters based on correlationship and statistical characteristics between data, which can perform classification more accurately. In addition, this paper uses difference data sets to reflect the patterns and regularities of the original data clearly, and constructs multiple fuzzy systems to consider various characteristics of the differences suitably. To verify effectiveness of the proposed techniques, this paper applies the constructed fuzzy systems to the field of time series prediction, and performs prediction for nonlinear time series examples.
Keywords
classification; hierarchical clustering; correlationship; difference data; multiple fuzzy systems;
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