Nature-Inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks: Volume 23 (Data Handling in Science and Technology") 〈23〉

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Nature-Inspired Methods in Chemometrics: Genetic Algorithms and Artificial Neural Networks: Volume 23 (Data Handling in Science and Technology") 〈23〉

  • ウェブストア価格 ¥56,368（本体¥51,244）
  • Elsevier Science（2003/12発売）
  • 外貨定価 US$ 255.00
  • 【ウェブストア限定】洋書・洋古書ポイント5倍対象商品（～2/28）
  • ポイント 2,560pt
  • オンデマンド(OD/POD)版です。キャンセルは承れません。
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  • 製本 Hardcover:ハードカバー版／ページ数 402 p.
  • 言語 ENG
  • 商品コード 9780444513502
  • DDC分類 006.32

基本説明

Hybrid genetic algorithms; Robust soft sensor development using genetic programming. Neural networks for the calibration of voltammetric data; and more.

Full Description


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.

Contents

PART IChapter 1: Genetic Algorithms and BeyondChapter 2: Hybrid Genetic AlgorithmsChapter 3: Robust Soft Sensor Development Using Genetic ProgrammingChapter 4: Genetic Algorithms in Molecular Modeling: a ReviewChapter 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 setsPART II: ARTIFICIAL NEURAL NETWORKSChapter 7: Basics of Artificial Neural Networks Chapter 8: Artificial Neural Networks in Molecular Structures-Property StudiesChapter 9: Neural Networks for the Calibration of Voltammetric DataChapter 10: Neural Networks and Genetic Algorithms Applications in Nuclear Magnetic Resonance (NMR) SpectroscopyChapter 11: A QSAR Model for Predicting the Acute Toxicity of Pesticides to GammaridsCONCLUSIONChapter 12: Applying Genetic Algorithms and Neural Networks to Chemometric Problems