期刊文献+

基于非线性主成分神经网络水泥强度预测研究 被引量:2

The Prediction Research about the Strength of Cement Based on the Nonliner Principal Component Neural Network
原文传递
导出
摘要 研究非线性主成分分析法与神经网络算法的融合模型,并将非线性主成分神经网络融合模型应用于水泥强度的预测研究,得到的结果表明预测误差很小,可见研究结果可用于指导水泥生产实践. The study of this disquisition is about the fusion model of the nonliner PCA (principal component analysis) and artificial neural network algorithm; applies the model to the prediction research about the strength of cement; then comes to the conclusion that the Prediction Error is very little. Therefore, the study of this disquisition can be used to guide the production practice of cement.
出处 《数学的实践与认识》 CSCD 北大核心 2013年第3期83-91,共9页 Mathematics in Practice and Theory
基金 江西省自然科学基金(20122BAB201016) 景德镇市科技计划(工业支撑)项目(2012JGY-1-11) 景德镇市社联社科规划项目
关键词 非线性主成分分析 神经网络 协方差函数 非线性主成分神经网络融合 模型 水泥强度 预测 nonliner principal component an.ulysis artificial neural network fusion model ofthe nonliner principal component -neural network strength of cement, prediction
  • 相关文献

参考文献7

二级参考文献14

  • 1韦振中,黄廷磊.基于支持向量机和遗传算法的特征选择[J].广西工学院学报,2006,17(2):18-21. 被引量:12
  • 2S. TAN, M. MAVROVOUNIOTIS, Reducing data dimensionality through optimizing Neural Network inputs[J]. AICHE Journal, 1995,41 (6): 135- 139
  • 3MARK A. KRAMER, Nonlinear Principal Component Analysis Using Autoassociative Neural Network[J]. AICHE Journal, 1991,137(2) :43 - 49
  • 4VENKATRMANAN. REDDY, Analysis of Plant Measures trough Input - training Neural Networks [J]. Computers chem, 1996,20: 889 - 894
  • 5J. EDWARD JACKSON, Principal Components and Factor Analysis:Part I - Principal Components[J]. Journal of Quality Technology, 1980,12(4) :77-84
  • 6CHEN S., COWAN, C. F. N., Orthogonal least squares learning algorithm for radial basis function networks, IEEE transactions on Neural networks[ J].1991, (2) :320 - 308
  • 7D.DONG, T.J. MCAVOY, Nonlinear Principal Component AnalysisBased on Principal Curves and Neural Networks[J]. Computers and Chem. Eng.,1996,20(1) :65 - 78
  • 8JIA - HUI JIANG, JI - HONG WANG, Neural network learning to non - linear principal component analysis[J]. Analytica Chimica Acta, 1996, (336):209 - 222
  • 9Vapnik V N. Statistical learning theory[M]. New York: Addison Wiley, 1998.
  • 10Baudat G,Anouar F. Generalized discriminant analysis using a kernel function[J]. Neural Computing, 2000,12(10):2385~2404.

共引文献51

同被引文献33

引证文献2

二级引证文献19

相关作者

内容加载中请稍等...

相关机构

内容加载中请稍等...

相关主题

内容加载中请稍等...

浏览历史

内容加载中请稍等...
;
使用帮助 返回顶部