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PLS Algorithm (pl + algorithm)
Selected AbstractsIntegrating fault detection and isolation with model predictive controlINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 4 2005Barry Lennox Abstract This paper illustrates how the application of partial least squares (PLS) can be extended to provide an integrated solution to fault detection and isolation, inferential estimation and model predictive control. It is shown that if PLS is used to identify a dynamic model of a plant then the latent variables of the model can identify the suitability of using this model under current conditions. This functionality enables automated model switching in piecewise linear systems. A further advantage of the proposed technique is that the inner structure of the model can be used to provide fault detection and isolation capabilities. By extending the approach to control systems and integrating a dynamic model, identified using the PLS algorithm, within a model predictive controller, similar benefits, such as automatic model selection can be achieved for the control system. The proposed approach is illustrated through its application to the Tennessee Eastman challenge process. Copyright © 2004 John Wiley & Sons, Ltd. [source] The PLS model space revisitedJOURNAL OF CHEMOMETRICS, Issue 2 2009Svante Wold Abstract Pell, Ramos and Manne (PRM) in a recent article in this journal claim that the ,conventional' PLS algorithm with orthogonal scores has an inherent inconsistency in that it uses different model spaces for calculating the prediction model coefficients and for calculating the X -space model and it's residuals [1]. We disagree with PRM. All PLS model scores, residuals, coefficients, etc., obtained by the conventional PLS algorithm do come from the same underlying latent variable (LV) model, and not from different models or model spaces as PRM suggest. PRM have simply posed a different model with different assumptions and obtained slightly different results, as should have been expected. Copyright © 2008 John Wiley & Sons, Ltd. [source] Erratum: On the numerical stability of two widely used PLS algorithmsJOURNAL OF CHEMOMETRICS, Issue 6 2008Nicolaas (Klaas) M. Faber The above article (DOI: 10.1002/cem.1112) was published online on 14 February 2008. An error was subsequently identified: the captions for Figures 1 and 2 were omitted; they should read as follows: Figure 1. Orthogonality criterion (,A) for the octane data as a function of number of components (A) calculated using the standard PLS algorithm and SIMPLS. Figure 2. Orthogonality criterion (,A) for the wines data as a function of number of components (A) calculated using the standard PLS algorithm and SIMPLS. [source] Some theoretical properties of the O-PLS methodJOURNAL OF CHEMOMETRICS, Issue 2 2004Thomas Verron Abstract The objective of this paper is to present new properties of the orthogonal projections to latent structures (O-PLS) method developed by Trygg and Wold (J. Chemometrics 2002; 16: 119,128). The original orthogonal signal correction (OSC) filter of Wold et al. (Chemometrics Intell. Lab. Syst. 1998; 44: 175,185) removes systematic variation from X that is unrelated to Y. O-PLS is a more restrictive OSC filter. O-PLS removes only systematic variation in X explained in each PLS component that is not correlated with Y. O-PLS is a slight modification of the NIPALS PLS algorithm, which should make O-PLS a generally applicable preprocessing and filtering method. The computation of the O-PLS components under the constraint of being correlated with one PLS component imposes particular properties on the space spanned by the O-PLS components. This paper is divided into two main sections. First we give an application of O-PLS on near-infrared reflectance spectra of soil samples, showing some graphical properties. Then we give the mathematical justifications of these properties. Copyright © 2004 John Wiley & Sons, Ltd. [source] Changes on Enological Parameters of White Wine Packaged in Bag-in-Box during Secondary Shelf LifeJOURNAL OF FOOD SCIENCE, Issue 8 2009Y. Fu ABSTRACT:, This study investigated the effects of temperature (22, 35, and 45 °C), storage time (48, 30, and 15 d), and packaging type on the quality of white wine in bag-in-box (BIB) during the secondary shelf life. Several enological parameters (color and contents of free and total SO2, total aldehyde, and total phenol) were monitored and correlated with oxygen transmission rate (OTR) and Fourier transform infrared (FTIR) spectral data. Time and temperature had significant effects on color development and SO2 depletion during storage. The increased absorbance at 420 nm was correlated with decreases of free SO2 and total SO2. Overall, total phenol content correlated negatively with total aldehyde content. The variance of the enological parameters can be correlated with the OTR data, indicating the barrier properties for the tested packages were different. FTIR,ATR spectra of the wine were analyzed chemometrically using PLS algorithm. The resulting models were able to predict the,A420, free SO2, total SO2, total phenol, total aldehyde, and storage time of the wines. This technique can potentially be used as an efficient tool to evaluate the quality of wine. [source] | ||||||||||