A three-part input-output clustering-based approach to fuzzy system identification

Sj Lee*, Xiao Jun Zeng

*Corresponding author for this work

研究成果: Conference contribution同行評審

2 引文 斯高帕斯(Scopus)

摘要

This article presents a clustering-based approach to fuzzy system identification. In order to construct an effective initial fuzzy model, this article tries to present a modular method to identify fuzzy systems based on a hybrid clustering-based technique. Moreover, the determination of the proper number of clusters and the appropriate location of clusters are one of primary considerations on constructing an effective initial fuzzy model. Due to the above reasons, a hybrid clustering algorithm concerning input, output, generalization and specialization has hence been introduced in this article. Further, the proposed clustering technique, three-part input-output clustering algorithm, integrates a variety of clustering features simultaneously, including the advantages of input clustering, output clustering, flat clustering, and hierarchical clustering, to effectively perform the identification of clustering problem.

原文English
主出版物標題Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
頁面55-60
頁數6
DOIs
出版狀態Published - 1 十二月 2010
事件2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10 - Cairo, Egypt
持續時間: 29 十一月 20101 十二月 2010

出版系列

名字Proceedings of the 2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10

Conference

Conference2010 10th International Conference on Intelligent Systems Design and Applications, ISDA'10
國家Egypt
城市Cairo
期間29/11/101/12/10

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