基于主成分的遗传神经网络股票指数预测研究

Based on PCA of genetic neural network prediction of stock index

数据预测在金融投资领域占有重要地位,预测中输入变量的选取影响着预测的速度和精度,传统方法选取输入变量主观性较强,预测效果欠佳。将遗传算法与BP网络结合,利用GA的全局搜索优化BP网络的结构参数,有效克服BP算法的局部收敛等问题。使用主成分分析法选取输入变量,并将GA—BP混合建模应用于沪市综合指数预测中。实验结果表明,该方法改善了预测精度,达到了较好的预测效果。

Data forecast occupies an important position in the financial investment field,the selected input variables affect the speed and accuracy of forecasts,traditional methods of selecting input variables subjective,and forecast ineffective.Combine Genetie Algorithms（GA） with BP neural network,using GA＇s global to search optimized BP network structure parameters,overcome the local convergence and other issues of BP algorithm effectively.Using Principal Component Analysis （PCA） to select input variables,and GA-BP hybrid modeling is applied to the Shanghai stock index prediction.Experimental results show that this method improves the prediction accuracy and achieves a better prediction.