基于径向基神经网络的局域预测法及其应用  

Local-region Prediction and Its Application Based on RBFNN

作　　者：修妍[1] 马军海[1] 

机构地区：[1]天津大学管理学院

出　　处：《计算机工程》2008年第9期19-21,共3页Computer Engineering

基　　金：国家自然科学基金资助项目(60641006);天津市高等学校科技发展基金资助项目(20052171)

摘　　要：一般的加权一阶局域预测法是利用最小二乘法求解模型,从而对混沌时序进行预测。基于径向基神经网络的局域预测法是在加权一阶局域预测模型的理论基础上,应用径向基神经网络代替加权一阶局域预测模型构成了基于径向基神经网络的局域预测模型。通过对Logistic映射以及Lorenz系统的3个分量的混沌时间序列的预测仿真,表明该预测方法对混沌时间序列的预测具有较好的效果。An add-weighted one-rank local-region linear prediction model is generally required by using the least squares method to predict the chaotic time series. A local-region linear prediction method based on Radial Basis Function Neural Net（RBFNN） is presented for chaotic time series prediction, which theory foundation is add-weighted one-rank local-region Single-step method. The prediction method is built by using RBFNN substitute for add-weighted one-rank model. The Logistic map and the three axes of Lorenz system are applied to verify the method. Simulation results indicate that the method is effective for the prediction of chaotic time series.

关 键 词：混沌时序 相空间重构 RBF神经网络 局域预测 

分 类 号：TP183[自动化与计算机技术—控制理论与控制工程]