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Neural Networks

Methodology and Applications

  • G. Dreyfus

Table of contents

  1. Front Matter
    Pages i-xviii
  2. G. Dreyfus
    Pages 1-83
  3. M. Samuelides
    Pages 289-327
  4. M. B. Gordon
    Pages 329-377
  5. F. Badran, M. Yacoub, S. Thiria
    Pages 379-442
  6. Back Matter
    Pages 491-497

About this book

Introduction

Neural networks represent a powerful data processing technique that has reached maturity and broad application. When clearly understood and appropriately used, they are a mandatory component in the toolbox of any engineer who wants make the best use of the available data, in order to build models, make predictions, mine data, recognize shapes or signals, etc. Ranging from theoretical foundations to real-life applications, this book is intended to provide engineers and researchers with clear methodologies for taking advantage of neural networks in industrial, financial or banking applications, many instances of which are presented in the book. For the benefit of readers wishing to gain deeper knowledge of the topics, the book features appendices that provide theoretical details for greater insight, and algorithmic details for efficient programming and implementation. The chapters have been written by experts ands seemlessly edited to present a coherent and comprehensive, yet not redundant, practically-oriented introduction.

Keywords

Data analysis Markov models Neural networks Pattern recognition Robotics classification dynamical systems learning optimization

Authors and affiliations

  • G. Dreyfus
    • 1
  1. 1.ESPCILaboratoire d’ÉlectroniqueParisFrance

Bibliographic information

  • DOI https://doi.org/10.1007/3-540-28847-3
  • Copyright Information Springer-Verlag Berlin Heidelberg 2005
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Physics and Astronomy
  • Print ISBN 978-3-540-22980-3
  • Online ISBN 978-3-540-28847-3
  • Buy this book on publisher's site