Title :
A Modeling Method for Time Series in Complex Industrial System
Author :
Xiao, Dong ; Mao, Zhi-zhong ; Pan, Xiao-Li ; Jia, Ming-Xing ; Wang, Fu-li
Author_Institution :
Key lab. of Process Ind. Autom. of Minist. of Educ. & Liaoning Province, Northeastern Univ., Shenyang
Abstract :
The data of complex industrial system were usually arrayed in the form of time series. This paper put forward the multivariate time-delayed principal component regression (MTPCR) method, which utilized the historical time series in the production process so as to establish a systematic prediction model. This method can calculate the delayed time of each input and output tunnel by which the modeling data were selected. The model established can predict the production outcome and product quality accurately in accordance with real-time input. With the aid of Simulink data and Matlab arithmetic, this paper concludes that MTPCR method possesses higher precision compared with other method
Keywords :
delays; manufacturing processes; manufacturing systems; modelling; principal component analysis; regression analysis; time series; Matlab arithmetic; Simulink data; complex industrial system; modeling method; multivariate time-delayed principal component regression method; production process; systematic prediction model; time series; Arithmetic; Autoregressive processes; Covariance matrix; Cybernetics; Delay effects; Job production systems; Laboratories; Machine learning; Mathematical model; Neural networks; Predictive models; Time series analysis; Autoregressive Moving Average (ARMA); Multivariate Time-delayed Principal Component Regression (MTPCR); Time series;
Conference_Titel :
Machine Learning and Cybernetics, 2006 International Conference on
Conference_Location :
Dalian, China
Print_ISBN :
1-4244-0061-9
DOI :
10.1109/ICMLC.2006.258507
