Regression models are well known and widely used as one of the important categories of models in system modeling. In this paper, we extend the concept of fuzzy regression in order to handle real-time implementation of data analysis of information granules. An ultimate objective of this study is to develop a hybrid of a genetically-guided clustering algorithm called genetic algorithm-Fuzzy C-Means (GA-FCM) and a convex hull-based fuzzy regression approach being regarded as a potential solution to the formation of information granules. It is anticipated that the setting of Granular Computing will help us reduce the computing time, especially in case of real-time data analysis, as well as an overall computational complexity. We propose an efficient real-time granular fuzzy regression analysis based on the convex hull approach in which a Beneath-Beyond algorithm is employed to design a convex hull. In the proposed design setting, we emphasize a pivotal role of the convex hull approach, which becomes crucial in alleviating limitations of linear programming manifesting in system modeling.
|ホスト出版物のタイトル||IEEE International Conference on Fuzzy Systems|
|出版ステータス||Published - 2011|
|イベント||2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011 - Taipei|
継続期間: 2011 6月 27 → 2011 6月 30
|Other||2011 IEEE International Conference on Fuzzy Systems, FUZZ 2011|
|Period||11/6/27 → 11/6/30|
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