A modeling method for time series in complex industrial system

被引：0

作者：

Xiao, Dong ^{[1
]}

Mao, Zhi-Zhong ^{[1
]}

Pan, Xiao-Li ^{[1
]}

Jia, Ming-Xing ^{[1
]}

Wang, Fu-Li ^{[1
]}

机构：

[1] Northeastern Univ, Minist Educ, Key Lab Proc Industry Automat, Shenyang 110004, Peoples R China

来源：

PROCEEDINGS OF 2006 INTERNATIONAL CONFERENCE ON MACHINE LEARNING AND CYBERNETICS, VOLS 1-7 | 2006年

基金：

美国国家科学基金会;

关键词：

time series; Multivariate Time-Delayed Principal Component Regression (MTPCR); Autoregressive Moving Average (ARMA);

D O I：

暂无

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

页码：3423 / +

页数：3

共 12 条

[1] Application of PCA and time series analysis in studies of precipitation in Tricity (Poland) [J].

Astel, A ;

Mazerski, J ;

Polkowska, Z ;

Namiesnik, J .

ADVANCES IN ENVIRONMENTAL RESEARCH, 2004, 8 (3-4) :337-349

[2]

BOX GEP, 2004, TIME SERIES ANAL FOR, P60

[3] Delayed time series predictions with neural networks [J].

Conway, AJ ;

Macpherson, KP ;

Brown, JC .

NEUROCOMPUTING, 1998, 18 (1-3) :81-89

[4] A dynamic artificial neural network model for forecasting time series events [J].

Ghiassi, M ;

Saidane, H ;

Zimbra, DK .

INTERNATIONAL JOURNAL OF FORECASTING, 2005, 21 (02) :341-362

[5] Effective dimensionality for principal component analysis of time series expression data [J].

Hörnquist, M ;

Hertz, J ;

Wahde, M .

BIOSYSTEMS, 2003, 71 (03) :311-317

[6] Artificial neural networks for non-stationary time series [J].

Kim, TY ;

Oh, KJ ;

Kim, CH ;

Do, JD .

NEUROCOMPUTING, 2004, 61 :439-447

[7] Nonlinear prediction of near-surface temperature via univariate and multivariate time series embedding [J].

Koçak, K ;

Saylan, L ;

Eitzinger, J .

ECOLOGICAL MODELLING, 2004, 173 (01) :1-7

[8] Potential assessment of a neural network model with PCA/RBF approach for forecasting pollutant trends in Mong Kok urban air, Hong Kong [J].

Lu, WZ ;

Wang, WJ ;

Wang, XK ;

Yan, SH ;

Lam, JC .

ENVIRONMENTAL RESEARCH, 2004, 96 (01) :79-87

[9] Fuzzy C-means clustering and principal component analysis of time series from near-infrared imaging of forearm ischemia [J].

Mansfield, JR ;

Sowa, MG ;

Scarth, GB ;

Somorjai, RL ;

Mantsch, HH .

COMPUTERIZED MEDICAL IMAGING AND GRAPHICS, 1997, 21 (05) :299-308

[10] Evolving the neural network model for forecasting air pollution time series [J].

Niska, H ;

Hiltunen, T ;

Karppinen, A ;

Ruuskanen, J ;

Kolehmainen, M .

ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2004, 17 (02) :159-167

← 1 2 →