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Neural Networks: Computational Models and Applications (Studies in Computational Intelligence, 53) Hardback - 2007
by Tang, Huajin; Tan, Kay Chen; Yi, Zhang
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- Hardback
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Details
- Title Neural Networks: Computational Models and Applications (Studies in Computational Intelligence, 53)
- Author Tang, Huajin; Tan, Kay Chen; Yi, Zhang
- Binding Hardback
- Edition 1st
- Condition New
- Pages 300
- Volumes 1
- Language ENG
- Publisher Springer
- Publication date 2007-03-12
- Illustrated Yes
- Features Bibliography, Illustrated, Table of Contents
- Bookseller's Inventory # Q-3540692258
- ISBN 9783540692256 / 3540692258
- Weight 1.2 lbs (0.54 kg)
- Dimensions 9.3 x 6.4 x 0.7 in (23.62 x 16.26 x 1.78 cm)
- Category Mathematics
- Library of Congress Catalogue Number 2006939344
- Dewey Decimal Code 006.32
- Quantity available 1
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From the publisher
From the rear cover
Neural Networks: Computational Models and Applications covers a wealth of important theoretical and practical issues in neural networks, including the learning algorithms of feed-forward neural networks, various dynamical properties of recurrent neural networks, winner-take-all networks and their applications in broad manifolds of computational intelligence: pattern recognition, uniform approximation, constrained optimization, NP-hard problems, and image segmentation. By presenting various computational models, this book is developed to provide readers with a quick but insightful understanding of the broad and rapidly growing areas in the neural networks domain.
Besides laying down fundamentals on artificial neural networks, this book also studies biologically inspired neural networks. Some typical computational models are discussed, and subsequently applied to objection recognition, scene analysis and associative memory. The studies of bio-inspired models have important implications in computer vision and robotic navigation, as well as new efficient algorithms for image analysis. Another significant feature of the book is that it begins with fundamental dynamical problems in presenting the mathematical techniques extensively used in analyzing neurodynamics, thus allowing non-mathematicians to develop and apply these analytical techniques easily.
Written for a wide readership, engineers, computer scientists and mathematicians interested in machine learning, data mining and neural networks modeling will find this book of value. This book will also act as a helpful reference for graduate students studying neural networks and complex dynamical systems.
