Fuzzy regression with radial basis function network

淡江大學機構典藏 > 商管學院 > 資訊管理學系暨研究所 > 期刊論文 > Item 987654321/68571

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題名:	Fuzzy regression with radial basis function network
作者:	鄭啟斌;Lee, E. S.
貢獻者:	淡江大學資訊管理學系
關鍵詞:	Regression analysis;Nonparametric fuzzy regression;Fuzzy radial basis network
日期:	2001-09-28
上傳時間:	2011-10-23 13:17:07 (UTC+8)
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.
關聯:	Fuzzy Sets and Systems 119, pp.291-301
DOI:	10.1016/S0165-0114(99)00098-6
顯示於類別:	[資訊管理學系暨研究所] 期刊論文

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