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A Design of an Improved Linguistic Model based on Information Granules  

Han, Yun-Hee (Dept. of Control and Instrumentation, Chosun University)
Kwak, Keun-Chang (Dept. of Control, Instrumentation, and Robot Engineering, Chosun University)
Publication Information
Journal of the Institute of Electronics Engineers of Korea CI / v.47, no.3, 2010 , pp. 76-82 More about this Journal
Abstract
In this paper, we develop Linguistic Model (LM) based on information granules as a systematic approach to generating fuzzy if-then rules from a given input-output data. The LM introduced by Pedrycz is performed by fuzzy information granulation obtained from Context-based Fuzzy Clustering(CFC). This clustering estimates clusters by preserving the homogeneity of the clustered patterns associated with the input and output data. Although the effectiveness of LM has been demonstrated in the previous works, it needs to improve in the sense of performance. Therefore, we focus on the automatic generation of linguistic contexts, addition of bias term, and the transformed form of consequent parameter to improve both approximation and generalization capability of the conventional LM. The experimental results revealed that the improved LM yielded a better performance in comparison with LM and the conventional works for automobile MPG(miles per gallon) predication and Boston housing data.
Keywords
linguistic model; information granules; context-based fuzzy clustering; linguistic contexts;
Citations & Related Records
Times Cited By KSCI : 1  (Citation Analysis)
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