Fuzzy regression with radial basis function network
學年 90
學期 1
出版（發表）日期 2001-09-28
作品名稱 Fuzzy regression with radial basis function network
作品名稱（其他語言）
著者 鄭啟斌; Lee, E. S.
單位 淡江大學資訊管理學系
出版者
著錄名稱、卷期、頁數 Fuzzy Sets and Systems 119, pp.291-301
摘要 Radial basis function network is used in fuzzy regression analysis without predefined functional relationship between the input and the output. The proposed approach is a fuzzification of the connection weights between the hidden and the output layers. This fuzzy network is trained by a hybrid learning algorithm, where self-organized learning is used for training the parameters of the hidden units and supervised learning is used for updating the weights between the hidden and the output layers. The c-mean clustering method and the k-nearest-neighbor heuristics are used for the self-organized learning. The supervised learning is carried out by solving a linear possibilistic programming problem. Techniques for the generalization of the network are also proposed. Numerical examples are used to illustrate and to test the performances of the approach.
關鍵字 Regression analysis;Nonparametric fuzzy regression;Fuzzy radial basis network
語言 en
ISSN 0165-0114 1872-6801
期刊性質 國外
收錄於
產學合作
通訊作者
審稿制度 否
國別 NLD
公開徵稿
出版型式 ,電子版,紙本
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