Overview

As an extension of artificial intelligence research, artificial neural networks (ANN) aim to simulate intelligent behavior by mimicking the way that biological neural networks function. In Artificial Neural Networks, an international panel of experts report the history of the application of ANN to chemical and biological problems, provide a guide to network architectures, training and the extraction of rules from trained networks, and cover many cutting-edge examples of the application of ANN to chemistry and biology. In the tradition of the highly successful Methods in Molecular Biology™ series, this volume exhibits clear, easy-to-use information with many step-by-step laboratory protocols. Comprehensive and state-of-the-art, Artificial Neural Networks is an excellent guide to this accelerating technological field of study.

Full Product Details

Author:   David J. Livingstone
Publisher:   Humana Press Inc.
Imprint:   Humana Press Inc.
Edition:   2009 ed.
Volume:   458
Dimensions:   Width: 15.50cm , Height: 1.50cm , Length: 23.50cm
Weight:   0.571kg
ISBN:  

9781588297181

ISBN 10:   1588297187
Pages:   254
Publication Date:   08 October 2008
Audience:   Professional and scholarly ,  College/higher education ,  Professional & Vocational ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   In Print  
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Table of Contents

Artificial Neural Networks in Biology and Chemistry—The Evolution of a New Analytical Tool.- Overview of Artificial Neural Networks.- Bayesian Regularization of Neural Networks.- Kohonen and Counterpropagation Neural Networks Applied for Mapping and Interpretation of IR Spectra.- Artificial Neural Network Modeling in Environmental Toxicology.- Neural Networks in Analytical Chemistry.- Application of Artificial Neural Networks for Decision Support in Medicine.- Neural Networks in Building QSAR Models.- Peptide Bioinformatics- Peptide Classification Using Peptide Machines.- Associative Neural Network.- Neural Networks Predict Protein Structure and Function.- The Extraction of Information and Knowledge from Trained Neural Networks.

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