遗传算法优化BP神经网络的混沌时间序列预测  

Prediction method for chaotic time series of optimized BP neural network based on genetic algorithm

作　　者：李松[1] 罗勇[1] 张铭锐[1] 

机构地区：[1]河北大学管理学院,河北保定071002

出　　处：《计算机工程与应用》2011年第29期52-55,共4页Computer Engineering and Applications

基　　金：国家自然科学基金(the National Natural Science Foundation of China under Grant No.50478088);河北省高等学校人文社会科学研究重点项目(No.SKZD2011106)

摘　　要：为提高BP神经网络预测模型对混沌时间序列的预测精度,将改进的遗传算法和BP神经网络结合,提出了一种基于改进遗传算法优化BP神经网络的混沌时间序列预测方法。利用改进的遗传算法优化BP神经网络的权值和阈值,训练BP神经网络预测模型求得最优解。将该模型应用到几个典型的非线性系统进行预测仿真,验证了该算法的有效性,与BP神经网络预测模型的预测结果进行了比较,仿真结果表明该方法对混沌时间序列具有更好的非线性拟合能力和更高的预测精度。In order to improve forecasting model precision of BP neural network for chaotic time series, an improved prediction mothod for elaaotic time series of optimized BP neural network based on modified genetic algorithm is presented.The modified genetic algorithm is used to optimize the weights and thresholds of BP neural network, and then BP neural network is trained to search for the optimal solution.The availability of this prediction method is proved by predicting chaotic time series of several typical nonlinear systems.Compared with the forecasting results of BP neural network, the computer simulations have shown that the nonlinear fitting and precision of this prediction model are better than those of BP prediction model.

关 键 词：混沌理论 预测 反向传播(BP)神经网络 遗传算法 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]