Search results for "least square"
showing 10 items of 286 documents
Chromatographic multivariate quality control of pharmaceuticals giving strongly overlapped peaks based on the chromatogram profile
2004
In the present paper, the simultaneous quantification of two analytes showing strongly overlapped chromatographic peaks (alpha = 1.02), under the assumption that both available equipment and training of the laboratory staff are basic, is studied. A pharmaceutical preparation (Mutabase) containing two drugs of similar physicochemical properties (amitriptyline and perphenazine) is selected as case of study. The assays are carried out under realistic working conditions (i.e. routine testing laboratories). Uncertainty considerations are introduced in the study. A partial least squares model is directly applied to the chromatographic data (with no previous signal transformation) to perform quali…
Polarity study of ionic liquids with the solvatochromic dye Nile Red: a QSPR approach using in silico VolSurf+ descriptors
2016
The in silico VolSurfþ descriptors, accounting for both cationic and anionic structural features of ionic liquids (ILs) were used to develop a Partial Least Squares (PLS) model able to establish a Quantitative Structure Property Relationship (QSPR) correlation with their solvatochromic dye Nile Red polarity. The PLS model allowed prediction of ENR values for 116 ILs providing an in silico ILs polarity database.
Application of the modelling power approach to variable subset selection for GA-PLS QSAR models
2007
A previously developed function, the Modelling Power Plot, has been applied to QSARs developed using partial least squares (PLS) following variable selection from a genetic algorithm (GA). Modelling power (Mp) integrates the predictive and descriptive capabilities of a QSAR. With regard to QSARs for narcotic toxic potency, Mp was able to guide the optimal selection of variables using a GA. The results emphasise the importance of Mp to assess the success of the variable selection and that techniques such as PLS are more robust following variable selection.
Prediction of ionic liquid's heat capacity by means of their in silico principal properties
2016
The in silico principal properties (PPs) of ionic liquids (ILs), derived by means of the VolSurf+ approach, were used to develop a Partial Least Squares (PLS) model able to find a quantitative correlation among IL descriptors (accounting for both cationic and anionic structural features) and heat capacity values, providing affordable predictions validated by experimental Cp measurements for an external set of ILs. In silico predictions allowed the selection of a limited number of structurally different ILs with similar Cp values, providing the possibility to select an optimal IL according to efficiency, as well as to environmental and economic sustainability. The present general procedure, …
Modeling the chiral resolution ability of highly sulfated β-cyclodextrin for basic compounds in electrokinetic chromatography
2013
Abstract Despite the fact that extensive research in the field of enantioseparations by capillary electrophoresis has been carried out, it is difficult to predict whether a concrete chiral selector would be useful for the separation of a racemic compound. Hence, several experimental effort is necessary to test the abilities of individual chiral selectors, usually by trial and error procedures. Thus, the enantioseparation of a new racemate becomes a time- and money-consuming task. In this work, the ability of highly sulfated β-cyclodextrin (HS-β-CD) as chiral selector in electrokinetic chromatography (EKC) is modeled for the first time, using exclusively directly-available structural data of…
Online topology estimation for vector autoregressive processes in data networks
2017
An important problem in data sciences pertains to inferring causal interactions among a collection of time series. Upon modeling these as a vector autoregressive (VAR) process, this paper deals with estimating the model parameters to identify the underlying causality graph. To exploit the sparse connectivity of causality graphs, the proposed estimators minimize a group-Lasso regularized functional. To cope with real-time applications, big data setups, and possibly time-varying topologies, two online algorithms are presented to recover the sparse coefficients when observations are received sequentially. The proposed algorithms are inspired by the classic recursive least squares (RLS) algorit…
A fully-automated procedure for measuring the electrical parameters of an induction motor drive with rotor at standstill
2003
The paper presents an automatic procedure to measure at standstill the electrical parameters of an induction motor fed by a PWM voltage source inverter. The proposed procedure executes automatically three tests using only the available PWM inverter control technique to obtain the required motor supply voltages. It allows the measurement of all the T-form circuit electrical parameters starting from the nameplate data as data-entry. It uses only a current sensor and no voltage sensor and process on line the collected data samples with a fast and easy to implement recursive least squares algorithm. Effectiveness of the automated procedure has been proved both by simulation and experimental tes…
Sensorless Control of Induction-Motor Drive Based on Robust Kalman Filter and Adaptive Speed Estimation
2014
This paper deals with robust estimation of rotor flux and speed for sensorless control of motion control systems with an induction motor. Instead of using sixth-order extended Kalman filters (EKFs), rotor flux is estimated by means of a fourth-order descriptor-type robust KF, which explicitly takes into account motor parameter uncertainties, whereas the speed is estimated using a recursive least squares algorithm starting from the knowledge of the rotor flux itself. It is shown that the descriptor-type structure allows for a direct translation of parameter uncertainties into variations of the coefficients appearing in the model, and this improves the degree of robustness of the estimates. E…
Non-linear RLS-based algorithm for pattern classification
2006
A new non-linear recursive least squares (RLS) algorithm is presented in the context of pattern classification problems. The algorithm incorporates the non-linearity of the filter's output in the updating rules of the classical RLS algorithm. The proposed method yields lower stationary error levels when compared to the standard LMS and RLS algorithms in a classical application of pattern classification, such as the channel equalization problem.
Graph recursive least squares filter for topology inference in causal data processes
2017
In this paper, we introduce the concept of recursive least squares graph filters for online topology inference in data networks that are modelled as Causal Graph Processes (CGP). A Causal Graph Process (CGP) is an auto regressive process in the time series associated to different variables, and whose coefficients are the so-called graph filters, which are matrix polynomials with different orders of the graph adjacency matrix. Given the time series of data at different variables, the goal is to estimate these graph filters, hence the associated underlying adjacency matrix. Previously proposed algorithms have focused on a batch approach, assuming implicitly stationarity of the CGP. We propose…