| 改善径向基函数网络泛化性能的主成分分析法及应用研究 |
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| 引用本文： | 殷春霞,胡铁松,郭元裕. 改善径向基函数网络泛化性能的主成分分析法及应用研究[J]. 武汉大学学报(工学版), 2000, 33(2): 85-89 |
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| 作者姓名： | 殷春霞 胡铁松 郭元裕 |
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| 作者单位： | 武汉水利电力大学水利水电工程学院,武汉,430072 |
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| 摘 要： | 采用主成分分析法 (PCA)来改善径向基函数网络的泛化性能 ,理论上可以根据PCA方法中的主成分累积贡献率 ηK 决定RBF网络的输入层节点数 .实例研究证明 ,采用PCA方法后的RBF网络泛化性能良好
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| 关 键 词： | 人工神经网络 径向基函数 泛化性能 主成分分析法 |
| 修稿时间： | 1999-11-06 |
Principal component analysis method to improve generalization performance of radial basis function network and its application research |
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| YIN Chun-xia,HU Tie-song,GUO Yuan-yu. Principal component analysis method to improve generalization performance of radial basis function network and its application research[J]. Engineering Journal of Wuhan University, 2000, 33(2): 85-89 |
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| Authors: | YIN Chun-xia HU Tie-song GUO Yuan-yu |
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| Abstract: | The principal component analysis (PCA)method is investigated for improving generalization performance of radial basis function(RBF)network. It becomes theoretically available to determining the number of input neurons in RBF network based on the cumulative contribution rate η-K which is the result of PCA.Case study shows that the RBF network with introducing PCA method consistently outperforms conventional RBF network in root mean square error and model efficiency R-y. |
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| Keywords: | artificial neural network radial basis function generalization performance principle component analysis method |
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