基于核主成分降维的RBF网络降水预测
|
摘要
针对径向基函数神经网络(RBF网络)的隐层节点数、中心和宽度难以确定的问题,为提高网络性能,首先采用模糊聚类分析法对样本数据进行初始聚类,以初始分类间的最小距离作为初始宽度;然后引入正交最小二乘法训练出新的数据中心、个数及权值,修改宽度为当前数据中心间的最小距离;最后采用梯度下降法训练并调整中心、宽度及权值;几种算法进行的组合优化改进,使网络泛化性能更优.由于降水影响因子众多,采用了核主成分分析法(KPCA)对样本数据进行特征提取降维预处理.对广西5月3区的日降水量使用上述模型进行预报实验,结果表明,该模型具有较好的泛化性能,预报准确率高于T213降水预报模式,具有一定的推广价值.
Aiming at the problems that are difficult to identify the number of hidden layer nodes in the radial basis function neural network(RBF network),centers and widths,this paper,in order to improve the network performance,firstly used the fuzzy clustering analysis method to the sample data for the initial clustering with an initial classification of minimum distance between as the initial width;then,introduced the orthogonal least squares to train a new data center,the number and weights to modify the width between the current data center minimum distance;finally adopted the gradient descent to train and adjust the center,width and weight.Several algorithms were combined and optimized to better the generalization performance of network.Due to the rainfall affecting factors varied,The nuclear principal component analysis(KPCA)applied in the sample data pretreatment feature extraction dimension reduction.This method has been applied in 3 districts of Guangxi's daily precipitation in May,the results show that the model had a good generalization performance,the forecasting accuracy was higher than that of T213 precipitation forecast model,it has certain popular value.
Aiming at the problems that are difficult to identify the number of hidden layer nodes in the radial basis function neural network(RBF network),centers and widths,this paper,in order to improve the network performance,firstly used the fuzzy clustering analysis method to the sample data for the initial clustering with an initial classification of minimum distance between as the initial width;then,introduced the orthogonal least squares to train a new data center,the number and weights to modify the width between the current data center minimum distance;finally adopted the gradient descent to train and adjust the center,width and weight.Several algorithms were combined and optimized to better the generalization performance of network.Due to the rainfall affecting factors varied,The nuclear principal component analysis(KPCA)applied in the sample data pretreatment feature extraction dimension reduction.This method has been applied in 3 districts of Guangxi's daily precipitation in May,the results show that the model had a good generalization performance,the forecasting accuracy was higher than that of T213 precipitation forecast model,it has certain popular value.
引文
[1]梁苓.主成分分析法在企业经济效益综合评价中的应用[J].数学的实践与认识,2002,32(5):763-768.
[2]熊伟,等.基于聚类的核主成分分析方法在地震属性降维中的应用[C].中国地球物理学会第二十七届年会论文集,2011:590.
[3]王和勇,等.基于聚类的核主成分分析在特征提取中的应用[J].计算机科学,2005,32(4):64-66.
[4]吴洪艳,黄道平.基于特征向量提取的核主元分析法[J].计算机科学,2009,36(7):185-187.
[5]许世刚,索丽生.确定前向神经网络隐层节点数的模糊聚类分析法[J].河海大学学报,2001,29(3):18-20.
[6](美)Fredirc M.Ham Ivica Kostanic.神经计算原理[M].叶世伟,王海娟,译.北京:机械工业出版社,2007:101-108.
[7]梁斌梅,韦琳娜.改进的径向基神经网络预测模型[J].计算机仿真,2009,26(11):191-194.
[8]罗芳琼,吴建生,金龙.基于最小二乘支持向量机集成的降水预报模型[J].热带气象学报,2011,27(4):577-584.
[2]熊伟,等.基于聚类的核主成分分析方法在地震属性降维中的应用[C].中国地球物理学会第二十七届年会论文集,2011:590.
[3]王和勇,等.基于聚类的核主成分分析在特征提取中的应用[J].计算机科学,2005,32(4):64-66.
[4]吴洪艳,黄道平.基于特征向量提取的核主元分析法[J].计算机科学,2009,36(7):185-187.
[5]许世刚,索丽生.确定前向神经网络隐层节点数的模糊聚类分析法[J].河海大学学报,2001,29(3):18-20.
[6](美)Fredirc M.Ham Ivica Kostanic.神经计算原理[M].叶世伟,王海娟,译.北京:机械工业出版社,2007:101-108.
[7]梁斌梅,韦琳娜.改进的径向基神经网络预测模型[J].计算机仿真,2009,26(11):191-194.
[8]罗芳琼,吴建生,金龙.基于最小二乘支持向量机集成的降水预报模型[J].热带气象学报,2011,27(4):577-584.
版权所有：© 2023 中国地质图书馆 中国地质调查局地学文献中心