| 基于PCA-RBF神经网络模型的航空备件预测方法 |
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| 引用本文： | 关子明,常文兵. 基于PCA-RBF神经网络模型的航空备件预测方法[J]. 北京工商大学学报(自然科学版), 2009, 27(3) |
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| 作者姓名： | 关子明 常文兵 |
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| 作者单位： | 北京航空航天大学,工程系统工程系,北京,100191 |
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| 摘 要： | 提出了基于PCA-RBF神经网络模型的备件预测方法.首先利用主成分分析去除原始输入层数据的相关性,以解决RBF神经网络模拟备件需求时输入变量过多、网络规模过大导致效率下降的问题,最后选择合适的径向基函数密度训练神经网络.通过结合实例进行分析,取得了较好的效果.
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| 关 键 词： | 备件预测 主成分分析 径向基函数神经网络 |
ESTIMATING APPROACH FOR AVIATION SPARE PARTS BASED ON PRINCIPAL COMPONENT ANALYSIS AND RBF ARTIFICIAL NEURAL NETWORK |
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| GUAN Zi-ming,CHANG Wen-bing. ESTIMATING APPROACH FOR AVIATION SPARE PARTS BASED ON PRINCIPAL COMPONENT ANALYSIS AND RBF ARTIFICIAL NEURAL NETWORK[J]. Journal of Beijing Technology and Business University:Natural Science Edition, 2009, 27(3) |
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| Authors: | GUAN Zi-ming CHANG Wen-bing |
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| Affiliation: | Department of System Engineering;Beijing University of Aeronautics and Astronautics;Beijing 100191;China |
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| Abstract: | Existing aviation material prediction approach has a low precision which can't meet the actual need.According to the problem,the forecasting approach for spare parts based on principal component analysis and artificial neural network was given.Firstly the approach can wipe off the correlation of the initial input level data,in order to solve the problem that RBF network has too many input factor when predicting and then the efficiency of the neural network descends because of bigger size,at the last we choo... |
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| Keywords: | spare parts prediction principal component analysis radial basis function artificial neural networks |
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