教師資料查詢 | 類別: 期刊論文 | 教師: 江正雄 CHIANG JEN-SHIUN (瀏覽個人網頁)

標題:An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network
學年102
學期2
出版(發表)日期2014/05/01
作品名稱An Approach for Fuzzy Modeling based on Self-Organizing Feature Maps Neural Network
作品名稱(其他語言)
著者Chen, Ching-yi; Chiang, Jen-Shiun; Chen, K. Y.; Liu, T. K.; Wong, Ching-Chang
單位淡江大學電機工程學系
出版者Bahrain: Natural Sciences Publishing Corporation
著錄名稱、卷期、頁數Applied Mathematics & Information Sciences 8(3), pp.1207-1215
摘要Exploration of large and high-dimensional data sets is one of the main problems in data analysis. Self-organizing feature maps (SOFM) is a powerful technique for clustering analysis and data mining. Competitive learning in the SOFM training process focuses on finding a neuron that its weight vector is most similar to that of an input vector. SOFM can be used to map large data sets to a simpler, usually one or two-dimensional topological structure. In this paper, we present a new approach to acquisition of initial fuzzy rules using SOFM learning algorithm, not only for its vector feature, but also for its topological. In general, fuzzy modeling requires two stages: structure identification and parameter learning. First, the algorithm partitions the input space into some local regions by using SOFM, then it determines the decision boundaries for local input regions, and finally, based on the decision boundaries, it learns the fuzzy rule for each local region by recursive least squares algorithm. The simulation results show that the proposed method can provide good model structure for fuzzy modeling and has high computing efficiency.
關鍵字
語言英文(美國)
ISSN2325-0399
期刊性質國外
收錄於SCI;
產學合作
通訊作者
審稿制度
國別巴林
公開徵稿
出版型式電子版
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