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Neural Networks: Computational Models and Applications

  • Huajin Tang
  • Kay Chen Tan
  • Zhang Yi

Part of the Studies in Computational Intelligence book series (SCI, volume 53)

About this book

Introduction

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.

Keywords

algorithms artificial neural network combinatorial optimization computer vision data mining image analysis learning linear optimization machine learning modeling navigation optimization proving robot

Authors and affiliations

  • Huajin Tang
    • 1
  • Kay Chen Tan
    • 2
  • Zhang Yi
    • 3
  1. 1.Queensland Brain InstituteUniversity of QueenslandQLD 4072Australia
  2. 2.Department of Electrical and Computer EngineeringNational University of Singapore117576Singapore
  3. 3.Computational Intelligence Laboratory School of Computer Science and EngineeringUniversity of Electronic Science and Technology of ChinaChengduP.R. China

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-69226-3
  • Copyright Information Springer Berlin Heidelberg 2007
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-69225-6
  • Online ISBN 978-3-540-69226-3
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site