關鍵字查詢 | 類別:期刊論文 | | 關鍵字:A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling

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序號 學年期 教師動態
1 100/1 電機系 李世安 副教授 期刊論文 發佈 A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling , [100-1] :A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling期刊論文A Clustering-based Algorithm to Extracting Fuzzy Rules for System ModelingChen, Ching-Yi; Li, Shin-An; Liu, Ta-Kang; Chen, Kuang-Yuan; Wong, Ching-Chang淡江大學電機工程學系Korea: Advanced Institute of Convergence ITInternational Journal of Advancements in Computing Technology 3(11), pp.394-401In this paper, a clustering-based algorithm is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed clustering method can automatically yield the number of clusters and its associated cluster centers from the input training data. While the features of training data are extracted by the proposed clustering method, the valuable information on the initial structure of the Sugeno-type fuzzy inference system is built up. For testing the performance of the proposed system modeling method, two wellknow examples from the literature and one r
2 100/1 電機系 陳光原 助理教授 期刊論文 發佈 A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling , [100-1] :A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling期刊論文A Clustering-based Algorithm to Extracting Fuzzy Rules for System ModelingChen, Ching-Yi; Li, Shin-An; Liu, Ta-Kang; Chen, Kuang-Yuan; Wong, Ching-Chang淡江大學電機工程學系Korea: Advanced Institute of Convergence ITInternational Journal of Advancements in Computing Technology 3(11), pp.394-401In this paper, a clustering-based algorithm is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed clustering method can automatically yield the number of clusters and its associated cluster centers from the input training data. While the features of training data are extracted by the proposed clustering method, the valuable information on the initial structure of the Sugeno-type fuzzy inference system is built up. For testing the performance of the proposed system modeling method, two wellknow examples from the literature and one r
3 100/1 電機系 翁慶昌 教授 期刊論文 發佈 A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling , [100-1] :A Clustering-based Algorithm to Extracting Fuzzy Rules for System Modeling期刊論文A Clustering-based Algorithm to Extracting Fuzzy Rules for System ModelingChen, Ching-Yi; Li, Shin-An; Liu, Ta-Kang; Chen, Kuang-Yuan; Wong, Ching-Chang淡江大學電機工程學系Korea: Advanced Institute of Convergence ITInternational Journal of Advancements in Computing Technology 3(11), pp.394-401In this paper, a clustering-based algorithm is proposed for automatically constructing a multi-input fuzzy model where only the input-output data of the identified system are available. The proposed clustering method can automatically yield the number of clusters and its associated cluster centers from the input training data. While the features of training data are extracted by the proposed clustering method, the valuable information on the initial structure of the Sugeno-type fuzzy inference system is built up. For testing the performance of the proposed system modeling method, two wellknow examples from the literature and one r
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