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Please use this identifier to cite or link to this item: http://hdl.handle.net/2080/1504

Title: Partial least squares: application in classification and multivariable process dynamics identification
Authors: Damarla, S K
Kavuri, N C
Kaushikaram, K S
Kundu, M
Keywords: PLS
MIMO
ARX
FIR
classification
identification
PRBS
Issue Date: Dec-2010
Citation: CHEMCON 2010, Annamalai University December 27-29th 2010, Annamalainagar, Chidambaram.
Abstract: Projection to latent structures or partial least squares (PLS) is a multivariable statistical regression method based on projecting/viewing the information in a high-dimensional data space down onto a low dimensional one defined by some latent variables. PLS is successfully applied in diverse fields including process monitoring; identification of process dynamics & control and deals with noisy and highly correlated data, quite often, only with a limited number of observations available. The conventional PLS is suitable for modeling time independent or steady state processes. For modeling dynamic process, the input data matrix (X) is augmented either with large number of lagged input variables (called finite impulse response (FIR) model) or including lagged input and output variables (called auto regressive model with exogenous input, ARX). By combining the PLS with ARX and FIR model structure, non-linear dynamic processes can be modeled. In the present study, PLS algorithm was used...
Description: Copyright belongs to proceeding publisher
URI: http://hdl.handle.net/2080/1504
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