Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks (Volume 23) (Data Handling in Science and Technology, Volume 23) [Hardcover] Leardi, Riccardo

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ISBN 10: 0444513507 / ISBN 13: 9780444513502

Edité par Elsevier Science, 2003

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Détails bibliographiques

• Titre: Nature-inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks (Volume 23) (Data Handling in Science and Technology, Volume 23) [Hardcover] Leardi, Riccardo
• Éditeur: Elsevier Science
• Date de parution: 2003
• Langue: anglais
• ISBN à 10 chiffres: 0444513507
• ISBN à 13 chiffres: 9780444513502
• Reliure: Couverture rigide
• État de l'article: Brand New

A propos de ce titre

Synopsis

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.

Présentation de l'éditeur

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