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

fYear

2006

fDate

13-16 Aug. 2006

Firstpage

3423

Lastpage

3427

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;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2006 International Conference on

Conference_Location

Dalian, China

Print_ISBN

1-4244-0061-9

Type

conf

DOI

10.1109/ICMLC.2006.258507

Filename

4028661