Prediction comparative study of complex multivariate systems with AGA-BP

摘要：

To improve the prediction accuracy of complex nonlinear systems(such as chaotic systems, power load and stock market),a novel scheme formed on the basis of AGA-BP neural network is proposed. According to Takens Theorem, nonlinear chaotic time series is reconstructed into vector data, AGA -BP neural network is used to fit the trained data of the predicted complex chaotic system, then the network parameters of data matrix built with the embedding dimensions are estimated,and the prediction value is also calculated. To evaluate the results, the proposed multivariate predictor based on AGA-BP neural network is compared with univariate one with the same numerical data. The simulation results obtained by the Lorenz system show that the prediction mean squared error of the multivariate predictor is much smaller than the univariate one.

作者: Su Liyun Liu Ruihua Li Fenglan Li Jiaojun

作者单位: School of Mathematics and Statistics Chongqing University of Technology Chongqing, China Library Chongqing University of Technology Chongqing, China School of Electronic Information Chongqing University of Technology Chongqing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Computer Application and System Modeling(2010计算机应用与系统建模国际会议 ICCASM 2010)

会议地点: 太原

会议语种:英文

页码: 50-54

在线出版日期: 2010-10-22（万方平台首次上网日期，不代表论文的发表时间）