Principles Of Artificial Neural Networks (3rd Edition)
eBook - ePub

Principles Of Artificial Neural Networks (3rd Edition)

  1. 500 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Principles Of Artificial Neural Networks (3rd Edition)

About this book

Artificial neural networks are most suitable for solving problems that are complex, ill-defined, highly nonlinear, of many and different variables, and/or stochastic. Such problems are abundant in medicine, in finance, in security and beyond.

This volume covers the basic theory and architecture of the major artificial neural networks. Uniquely, it presents 18 complete case studies of applications of neural networks in various fields, ranging from cell-shape classification to micro-trading in finance and to constellation recognition — all with their respective source codes. These case studies demonstrate to the readers in detail how such case studies are designed and executed and how their specific results are obtained.

The book is written for a one-semester graduate or senior-level undergraduate course on artificial neural networks. It is also intended to be a self-study and a reference text for scientists, engineers and for researchers in medicine, finance and data mining.

Contents:

  • Introduction and Role of Artificial Neural Networks
  • Fundamentals of Biological Neural Networks
  • Basic Principles of ANNs and Their Early Structures
  • The Perceptron
  • The Madaline
  • Back Propagation
  • Hopfield Networks
  • Counter Propagation
  • Large Scale Memory Storage and Retrieval (LAMSTAR) Network
  • Adaptive Resonance Theory
  • The Cognitron and the Neocognitron
  • Statistical Training
  • Recurrent (Time Cycling) Back Propagation Networks


Readership: Graduate and advanced senior students in artificial intelligence, pattern recognition & image analysis, neural networks, computational economics and finance, and biomedical engineering.

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Yes, you can access Principles Of Artificial Neural Networks (3rd Edition) by Daniel Graupe in PDF and/or ePUB format, as well as other popular books in Biological Sciences & Science General. We have over one million books available in our catalogue for you to explore.

Information

Publisher
WSPC
Year
2013
Print ISBN
9789814522731
eBook ISBN
9789814522755
Edition
3
Topic
Biological Sciences
Subtopic
Science General
Index
Biological Sciences

Table of contents

  1. Front Cover
  2. Half Title
  3. Author Title
  4. Title Page
  5. Copyright
  6. Dedication
  7. Contents
  8. Acknowledgments
  9. Preface to the Third Edition
  10. Preface to the Second Edition
  11. Preface to the First Edition
  12. Chapter 1. Introduction and Role of Artificial Neural Networks
  13. Chapter 2. Fundamentals of Biological Neural Networks
  14. Chapter 3. Basic Principles of ANNs and Their Early Structures
  15. Chapter 4. The Perceptron
  16. Chapter 5. The Madaline
  17. Chapter 6. Back Propagation
  18. Chapter 7. Hopfield Networks
  19. Chapter 8. Counter Propagation
  20. Chapter 9. Large Scale Memory Storage and Retrieval (LAMSTAR) Network
  21. Chapter 10. Adaptive Resonance Theory
  22. Chapter 11. The Cognitron and the Neocognitron
  23. Chapter 12. Statistical Training
  24. Chapter 13. Recurrent (Time Cycling) Back Propagation Networks
  25. Problems
  26. References
  27. Author Index
  28. Subject Index