
Nature-inspired methods in chemometrics genetic algorithms and artificial neural networks
Başlık:
Nature-inspired methods in chemometrics genetic algorithms and artificial neural networks
Yazar:
Leardi, R. (Riccardo)
ISBN:
9780444513502
Basım Bilgisi:
1st ed.
Yayın Bilgileri:
Amsterdam ; Boston : Elsevier, 2003.
Fiziksel Tanımlama:
xviii, 383 p. : ill. ; 25 cm.
Seri:
Data handling in science and technology, v. 23
Seri Başlığı:
Data handling in science and technology, 0922-3487 ; v. 23
İçerik:
PART I: GENETIC ALGORITHMS -- Chapter 1: Genetic Algorithms and Beyond -- Chapter 2: Hybrid Genetic Algorithms -- Chapter 3: Robust Soft Sensor Development Using Genetic Programming -- Chapter 4: Genetic Algorithms in Molecular Modeling: a Review -- Chapter 5: MobyDigs: Sofwtare for Regression and Classification Models by Genetic Algorithms. -- Chapter 6: Genetic Algorithm-PLS as a tool for wavelength selection in spectral data sets -- PART II: ARTIFICIAL NEURAL NETWORKS -- Chapter 7: Basics of Artificial Neural Networks -- Chapter 8: Artificial Neural Networks in Molecular Structures-Property Studies -- Chapter 9: Neural Networks for the Calibration of Voltammetric Data -- Chapter 10: Neural Networks and Genetic Algorithms Applications in Nuclear Magnetic Resonance (NMR) Spectroscopy -- Chapter 11: A QSAR Model for Predicting the Acute Toxicity of Pesticides to Gammarids -- CONCLUSION -- Chapter 12: Applying Genetic Algorithms and Neural Networks to Chemometric Problems.
Özet:
In recent years Genetic Algorithms (GA) and Artificial Neural Networks (ANN) have progressively increased in importance amongst the techniques routinely used in chemometrics. This book contains contributions from experts in the field is divided in two sections (GA and ANN). In each part, tutorial chapters are included in which the theoretical bases of each technique are expertly (but simply) described. These are followed by application chapters in which special emphasis will be given to the advantages of the application of GA or ANN to that specific problem, compared to classical techniques, and to the risks connected with its misuse. This book is of use to all those who are using or are interested in GA and ANN. Beginners can focus their attentions on the tutorials, whilst the most advanced readers will be more interested in looking at the applications of the techniques. It is also suitable as a reference book for students. - Subject matter is steadily increasing in importance - Comparison of Genetic Algorithms (GA) and Artificial Neural Networks (ANN) with the classical techniques - Suitable for both beginners and advanced researchers.
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ScienceDirect An electronic book accessible through the World Wide Web; click for informationPublisher description http://www.loc.gov/catdir/description/els041/2003049532.html
Table of contents http://www.loc.gov/catdir/toc/els041/2003049532.html