Andere Kunden interessierten sich auch für
![Control of Nonlinear Processes using Fuzzy logic and Neural Networks]( "Control of Nonlinear Processes using Fuzzy logic and Neural Networks")
Control of Nonlinear Processes using Fuzzy logic and Neural Networks55,99 €![A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks]( "A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks")
A Beginner's Guide to Medical Application Development with Deep Convolutional Neural Networks144,99 €![Industrial Applications of Neural Networks]( "Industrial Applications of Neural Networks")
Industrial Applications of Neural Networks187,99 €![Artificial Neural Networks in Food Processing]( "Artificial Neural Networks in Food Processing")
Artificial Neural Networks in Food Processing94,95 €![Implementation of Neural Network Techniques for the loading of Copper]( "Implementation of Neural Network Techniques for the loading of Copper")
Implementation of Neural Network Techniques for the loading of Copper26,99 €![Variable Refrigerant Flow Systems]( "Variable Refrigerant Flow Systems")
Variable Refrigerant Flow Systems117,99 €![Variable Refrigerant Flow Systems]( "Variable Refrigerant Flow Systems")
Variable Refrigerant Flow Systems117,99 €
This book focuses particularly on the application of chemometrics in the field of analytical chemistry. In infrared spectroscopy for instance, chemometrics consists in the prediction of a quantitative variable (the obtention of which is delicate, requiring a chemical analysis and a qualified operator), such as the concentration of a component present in the studied product from spectral data measured on various wavelengths or wavenumbers. In this book the author proposes a methodology in the field of chemometrics to handle the spectrophotometric data which are often represented in high dimension. To handle these data, a new incremental method (step-by-step) is proposed for the selection of spectral data using linear and non-linear regression. The author proposes, also, to improve the previous method by a judicious choice of the first selected variable, which has a very important influence on the final performances of the prediction. The idea is to use a measure of the mutual information between the independent and dependent variables to select the first one; then the previous incremental method (step-by-step) is used to select the next variables.