摘要
针对目前混沌时间序列预测模型预测结果差异较大的问题,归纳了4种混沌时间序列预测模型:BRF神经网络模型、最大Lyapunov指数模型、局域线性模型和Volterra滤波器自适应预测模型,并对这4种预测模型进行了比较研究。应用4种预测模型对几个典型的非线性系统进行预测仿真。结果表明,这4种预测模型对典型混沌时间序列预测都具有很好的预测效果;在预测精度上BRF模型和Volterra模型明显优于最大Lyapunov指数模型和局域线性模型。
Aiming at the prediction precision problem using prediction model for chaotic time series,the 4 prediction models based on chaos theory,such as Radial Basis Function neural network（RBF） model,Lyapunov exponent model,local-region prediction model and Voherra filter model are introduced.Besides,the 4 prediction models are comparative studied.The time series of several typical nonlinear systems are predicted by the 4 prediction models.The simulation results show that the proposed 4 prediction models have effective prediction results for typical nonlinear systems.The RBF model and the Volterra filter model have the better performances on the precision accuracy.
出处
《计算机工程与应用》
CSCD
北大核心
2009年第32期53-56,共4页
Computer Engineering and Applications
基金
国家自然科学基金No.50478088~~
关键词
时间序列
预测模型
混沌理论
相空间重构
time series
prediction model
chaos theory
phase space reconstruction
