摘要
在相空间重构理论的基础上,将改进的遗传算法和神经网络结合起来,提出了一种混合遗传神经网络预测混沌时间序列的方法。通过复相关法和Cao方法重构混沌时间序列,利用改进的遗传算法优化神经网络的结构、初始权值和阈值,然后训练神经网络求得最优解。该算法应用到混沌时间序列的预测中,验证了该算法的有效性,并与BP和RBF算法的预测精度进行了比较,仿真结果表明该算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。
By incorporating modified genetic algorithm with the neural network, a novel hybrid genetic neural network method for predicting chaotic time series based on the theory of phase-space reconstruction was presented. The chaotic time series was reconstructed by using multiple correlation and Cao＇s methods, and the modified genetic algorithm was used to optimize the structure, the initial weights and thresholds of neural network, then neural network was trained to search for the optimal solution. The availability of this algorithm was proved by predicting chaotic time series, and the precision of this algorithm compared with those of BP and RBF algorithms. The computer simulations have shown that the nonlinear fitting and precision of this algorithm are better than those of BP and RBF algorithms.
出处
《系统仿真学报》
EI
CAS
CSCD
北大核心
2008年第21期5825-5828,共4页
Journal of System Simulation
基金
国家自然科学基金项目(50677014)
高校博士点基金项目(20060532016)
教育部新世纪优秀人才支持计划(NCET-04-0767)
湖南省自然科学基金项目(06JJ2024)
关键词
混沌时间序列
相空间重构
遗传算法
神经网络
非线性预测
chaotic time series
phase-space reconstruction
genetic algorithm
neural network
nonlinear prediction
