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OverviewNeural-network models for event analysis are widely used in experimental high-energy physics, star/galaxy discrimination, control of adaptive optical systems, prediction of nuclear properties, fast interpolation of potential energy surfaces in chemistry, classification of mass spectra of organic compounds, protein-structure prediction, analysis of DNA sequences, and design of pharmaceuticals. This book, devoted to this highly interdisciplinary research area, addresses scientists and graduate students. The pedagogically written review articles range over a variety of fields including astronomy, nuclear physics, experimental particle physics, bioinformatics, linguistics, and information processing. Full Product DetailsAuthor: John W. Clark , Thomas Lindenau , Manfred L. RistigPublisher: Springer-Verlag Berlin and Heidelberg GmbH & Co. KG Imprint: Springer-Verlag Berlin and Heidelberg GmbH & Co. K Edition: Softcover reprint of the original 1st ed. 1999 Volume: 522 Dimensions: Width: 15.50cm , Height: 1.70cm , Length: 23.50cm Weight: 0.474kg ISBN: 9783662142356ISBN 10: 366214235 Pages: 290 Publication Date: 17 April 2014 Audience: Professional and scholarly , Professional & Vocational Format: Paperback Publisher's Status: Active Availability: Manufactured on demand  We will order this item for you from a manufactured on demand supplier. Table of ContentsNeural networks: New tools for modelling and data analysis in science.- Adaptive optics: Neural network wavefront sensing, reconstruction, and prediction.- Nuclear physics with neural networks.- Using neural networks to learn energy corrections in hadronic calorimeters.- Neural networks for protein structure prediction.- Evolution teaches neural networks to predict protein structure.- An application of artificial neural networks in linguistics.- Optimization with neural networks.- Dynamics of networks and applications.ReviewsAuthor InformationTab Content 6Author Website:Countries AvailableAll regions |
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