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MORE ABOUT THIS BOOK
Main description:
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.
Feature:
Serves as a detailed, easy-to-use guide to the application of artificial neural networks
Includes methods involving the mapping and interpretation of Infra Red spectra and modelling environmental toxicology
Back cover:
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.
Contents:
Chapter 1. Artificial Neural Networks in Biology and Chemistry - the Evolution of a new Analytical Tool Hugh M. Cartwright Chapter 2. Overview of Artificial Neural Networks Jinming Zou, Yi Han, and Sung-Sau So Chapter 3. Bayesian Regularization of Neural Networks Frank Burden and Dave Winkler Chapter 4. Kohonen and Counter-propagation Neural Networks Applied for Mapping and Interpretation of IR Spectra Marjana Novic Chapter 5. Artificial Neural Network Modeling in Environmental Toxicology James Devillers Chapter 6. Neural Networks in Analytical Chemistry Mehdi Jalali-Heravi Chapter 7. Application of Artificial Neural Networks for Decision Support in Medicine Brendan Larder, Dechao Wang and Andy Revell Chapter 8. Neural Networks in Building QSAR Models Igor I. Baskin, Vladimir A. Palyulin, and Nikolai S. Zefirov Chapter 9. Peptide Bioinformatics- Peptide Classification Using Peptide Machines Zheng Rong Yang Chapter 10. Associative Neural Network Igor V. Tetko Chapter 11. Neural Networks Predict Protein Structure and Function Marco Punta and Burkhard Rost Chapter 12. The Extraction of Information and Knowledge from Trained Neural Networks David J. Livingstone, Antony Browne, Raymond Crichton, Brian D. Hudson, David Whitley and Martyn G. Ford
PRODUCT DETAILS
Publisher: Springer (Humana Press)
Publication date: October, 2011
Pages: 254
Weight: 409g
Availability: Not available (reason unspecified)
Subcategories: Neuroscience
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