Many system identification techniques have been proposed over the last few decades, including ordinary and recursive least squares. Recently, Partial Least Squares (PLS) has become a popular tool in the chemometric community and is beginning to be applied to solve complex industrial process control problems. These studies have tended to ignore the issue of bias with this form of model and it is...

partial least square and principal component regression methods were applied to various mixtures of Allura Red and Brilliant Blue to determine the concentrations. Colorants, at the same time, were analyzed with UV-spectrophotometry in chemical separation. The obtained experimental data have been evaluated by chemometric methods as Partial Least Squares (PLS) and Principle Component Regressi...

Six simple, dynamic soft sensor methodologies with two update conditions were compared on two experimentally-obtained datasets and one simulated dataset. The soft sensors investigated were: moving window partial least squares regression (and a recursive variant), moving window random forest regression, feedforward neural networks, mean moving window, and a novel random forest partial least squa...

PLS univariate regression is a model linking a dependent variable y to a set X= {x1; : : : ; xp} of (numerical or categorical) explanatory variables. It can be obtained as a series of simple and multiple regressions. By taking advantage from the statistical tests associated with linear regression, it is feasible to select the signi6cant explanatory variables to include in PLS regression and to ...

In this work we find out how PLS algorithms, properly adjusted, can work as optimal scaling algorithms. This new feature of PLS, which had until now been totally unexplored, allowed us to devise a new suite of PLS methods: the Non-Metric PLS (NM-PLS) methods. Mots-clès: Analyse des données data mining, Problèmes inverses et sparsité

Partial least squares (PLS) regression on an L2-continuous stochastic process is an extension of the 2nite set case of predictor variables. The PLS components existence as eigenvectors of some operator and convergence properties of the PLS approximation are proved. The results of an application to stock-exchange data will be compared with those obtained by other methods. c © 2003 Elsevier B.V. ...

Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functio...

Olmesartan medoxamil (OLM, an angiotensin II receptor blocker), amlodipine besylate (AML, a dihydropyridine calcium channel blocker) and hydrochlorothiazide (HCT, a diuretic of the class of benzothiadiazines) are co-formulated in a single-dose combination for the treatment of hypertensive patients whose blood pressure is not adequately controlled on either component monotherapy. In this work, t...

In order to improve fitting and forecasting precision and solve the problem that some data with less sensitivity lead to low simulation precision of partial leastsquares regression (PLS for short) model, the new method of simulating freezing depth is presented according to ground temperature of different depths, air temperatures, surface temperatures and the like. Firstly, the PLS model, which ...

Reportedly, standard identiication algorithms do not guarantee the controllability of the estimated system. In this paper, a penalized least squares (PLS) identiication criterion is proposed to overcome this diiculty. The criterion is shown to provide estimated systems which exhibit an uniform controllability property through time. Moreover, the Lai and Wei upper bound for the least squares est...