Fuzzy regression with radial basis function network

Chi-Bin Cheng; E.Stanley Lee

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Fuzzy regression with radial basis function network

机译：径向基函数网络的模糊回归

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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.

机译：径向基函数网络用于模糊回归分析，而输入和输出之间没有预定义的函数关系。提出的方法是隐藏层和输出层之间的连接权重的模糊化。该模糊网络由混合学习算法训练，其中自组织学习用于训练隐藏单元的参数，监督学习用于更新隐藏层和输出层之间的权重。

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《Fuzzy sets and systems》 |2001年第2期|p.291-301|共11页
作者
Chi-Bin Cheng; E.Stanley Lee;
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收录信息美国《科学引文索引》(SCI);美国《工程索引》(EI);
原文格式 PDF
正文语种 eng
中图分类模糊数学;
关键词
Regression analysis; Nonparametric fuzzy regression; Fuzzy radial basis network;

机译：回归分析;非参数模糊回归;模糊径向基网络;

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Fuzzy regression with radial basis function network

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