Improved RBF Regression Using Fuzzy Partition and Supervised Fuzzy Clustering

Chen Cong; Wang Shi-Tong

doi:10.3724/SP.J.1146.2008.00350

Volume 31 Issue 5

Dec. 2010

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Chen Cong, Wang Shi-Tong. Improved RBF Regression Using Fuzzy Partition and Supervised Fuzzy Clustering[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1157-1160. doi: 10.3724/SP.J.1146.2008.00350

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Chen Cong, Wang Shi-Tong. Improved RBF Regression Using Fuzzy Partition and Supervised Fuzzy Clustering[J]. Journal of Electronics & Information Technology, 2009, 31(5): 1157-1160. doi: 10.3724/SP.J.1146.2008.00350

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Improved RBF Regression Using Fuzzy Partition and Supervised Fuzzy Clustering

doi: 10.3724/SP.J.1146.2008.00350

Received Date: 2008-03-31
Rev Recd Date: 2008-07-07
Publish Date: 2009-05-19

Abstract

Abstract

In order to improve the precision of RBF regression, this article advances a novel RBF regression modeling method using fuzzy partition and supervised clustering. The proposed method first splits the training data into several subsets using supervised clustering. Then local regression models are independently built with RBF network for each subset. Finally, the output of the network is formed with a weighted combination of each local model. Experiments show that the proposed method achieves more accurate interpretation of local behavior of the target model.

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References(1)

References

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