摘要
针对传统PCA(主元分析)股票价格预测方法在非线性过程应用中存在的缺点,本文提出了一种基于RBF神经网络的非线性PCA(NLPCA)方法,不仅提取了高维原始数据的线性信息还能提取非线性信息.在此基础上进一步提出了样本中误差的检测方法,仿真试验表明它能有效地减小误差点对网络训练精度的影响,大大增强了股票价格预测的准确性.
To overcome the shortcomings of the traditional PCA used in the stock price forecasting of nonlinear process, an approach of nonlinear principal component analysis（NLPCA）based on the RBF neural network is presented in this paper, which can extract not only the linear features but also the nonlinear ones in high dimensional data. Further more,a method of detecting the gross errors was presented based on this NLPCA algorithm, The simulation results show that this method successfully reduces the errors, effectively improves the precision of the prediction and the accuracy of the NLPCA algorithm.
出处
《吉林师范大学学报（自然科学版）》
2008年第4期70-73,共4页
Journal of Jilin Normal University:Natural Science Edition
基金
吉林省科技厅资助项目(20070322)
