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Identification of full profile disturbance models for sheet forming processes
Author(s) -
Rigopoulos Apostolos,
Arkun Yaman,
Kayihan Ferhan
Publication year - 1997
Publication title -
aiche journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.958
H-Index - 167
eISSN - 1547-5905
pISSN - 0001-1541
DOI - 10.1002/aic.690430318
Subject(s) - autoregressive model , weighting , disturbance (geology) , identification (biology) , algorithm , computer science , system identification , principal component analysis , model selection , control theory (sociology) , mathematics , data mining , control (management) , artificial intelligence , statistics , physics , paleontology , botany , acoustics , biology , measure (data warehouse)
In this article we present a method for the on‐line identification and modeling of full profile disturbance models for sheet forming processes. A particular principal components analysis technique called the Karhunen‐Loève expansion is used to adaptively identify the significant features of the profile. In addition, we show how the temporal modes of the reconstructed profile can be modeled using low‐order linear autoregressive (AR) processes. By simulation examples, the effect of the order of the AR model is studied, as well as the window size of the data used in the on‐line application of the KL expansion, the effect of data weighting, the importance of the correct selection of the number of modes, and the frequency of updating the parameters of the AR models. Identified disturbance models can be easily incorporated into model‐predictive control algorithms.