基于遗传算法优化混沌神经网络的股票指数预测

The Prediction of Stock Index Based on Genetic Algorithm Optimized Chaotic Neural Network

为提高BP神经网络预测模型对混沌时间序列的预测准确性,提出一种基于遗传算法优化BP神经网络的改进混沌时间序列预测方法。本文采用时间序列输入输出参数数量构造BP神经网络拓扑结构,利用遗传算法优化BP神经网络的权值和阈值,然后训练BP神经网络预测模型求得最优解,将该预测方法应用到上证综合指数的时间序列进行有效性验证,结果表明了该方法对上证综合指数具有更好的非线性拟合能力和更高的预测准确性。

In order to improve forecasting model accuracy of BP neural network for chaotic time series,an improved prediction method for chaotic time series of optimized BP neural network based on genetic algorithm （ GA） was presented. In this method,the BP neural network topology was constructed by the number of input and output of time series. The GA was used to optimize the weights and thresholds of BP neural network,and then BP neural network was trained to search for the optimal solution. The availability of the proposed prediction method was proved by predicting the time series of Shanghai stock index. The computer simulations have shown that the nonlinear fitting and accuracy of the modified prediction methods were better than BP prediction methods.