基于ISNN和HGA的沪深300指数预测方法

Hushen 300 index forecasting approach based on ISNN and HGA

提出了一种改进的结构化神经网络(ISNN),并基于ISNN构建了沪深300指数预测模型。设计了一种优化性能更好的混合遗传算法(HGA),并采用HGA对ISNN预测模型进行训练。应用训练好的预测模型对2007年上半年的沪深300指数日收盘价进行了预测分析。实验结果表明,该方法收敛速度快、学习能力强、预测精度较高、误差率较小。

This paper proposed an improved structure-based neural network （ISNN）and applied to construct a forecasting model for Hushen 300 index. Designed an outstanding hybrid genetic algorithm （HGA）and used to train the ISNN forecasting model. Evaluated the proposed approach by the Hushen 300 index of the first half year at 2007. Experimental results suggest that the proposed approach has more favorable characteristics such as the convergence rate, learning ability, forecasting precision and estimating error.