Skip to main content
SpringerLink
Log in
Book cover
  • Book
  • © 1995

Neural Networks

An Introduction

Authors:

  1. Berndt Müller

    1. Department of Physics, Duke University, Durham, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

  2. Joachim Reinhardt

    1. Institut für Theoretische Physik, J.-W.-Goethe-Universität, Frankfurt, Germany

    View author publications

    You can also search for this author in PubMed   Google Scholar

  3. Michael T. Strickland

    1. Department of Physics, Duke University, Durham, USA

    View author publications

    You can also search for this author in PubMed   Google Scholar

Part of the book series: Physics of Neural Networks (NEURAL NETWORKS)

Sections

Buy it now

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

This is a preview of subscription content, log in via an institution to check for access.

Table of contents (29 chapters)

  1. Front Matter

    Pages i-xv
    PDF

  2. Models of Neural Networks

    1. Front Matter

      Pages 1-1
      PDF

    2. The Structure of the Central Nervous System

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 3-12

    3. Neural Networks Introduced

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 13-23

    4. Associative Memory

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 24-37

    5. Stochastic Neurons

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 38-45

    6. Cybernetic Networks

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 46-51

    7. Multilayered Perceptrons

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 52-62

    8. Applications

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 63-71

    9. More Applications of Neural Networks

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 72-92

    10. Network Architecture and Generalization

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 93-107

    11. Associative Memory: Advanced Learning Strategies

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 108-125

    12. Combinatorial Optimization

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 126-134

    13. VLSI and Neural Networks

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 135-143

    14. Symmetrical Networks with Hidden Neurons

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 144-150

    15. Coupled Neural Networks

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 151-161

    16. Unsupervised Learning

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 162-173

    17. Evolutionary Algorithms for Learning

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 174-187

  3. Statistical Physics of Neural Networks

    1. Front Matter

      Pages 189-189
      PDF

    2. Statistical Physics and Spin Glasses

      • Berndt Müller, Joachim Reinhardt, Michael T. Strickland
      Pages 191-200
Back to top

About this book

Neural Networks presents concepts of neural-network models and techniques of parallel distributed processing in a three-step approach: - A brief overview of the neural structure of the brain and the history of neural-network modeling introduces to associative memory, preceptrons, feature-sensitive networks, learning strategies, and practical applications. - The second part covers subjects like statistical physics of spin glasses, the mean-field theory of the Hopfield model, and the "space of interactions" approach to the storage capacity of neural networks. - The final part discusses nine programs with practical demonstrations of neural-network models. The software and source code in C are on a 3 1/2" MS-DOS diskette can be run with Microsoft, Borland, Turbo-C, or compatible compilers.
Back to top

Keywords

Back to top

Reviews

"I have enjoyed using the previous edition of this well-known book both as a personal text and as a class manual. Although it claims to be only an introduction, it contains a wealth of material and addresses real problems in physics." Computing Reviews
Back to top

Authors and Affiliations

  • Department of Physics, Duke University, Durham, USA

    Berndt Müller, Michael T. Strickland

  • Institut für Theoretische Physik, J.-W.-Goethe-Universität, Frankfurt, Germany

    Joachim Reinhardt

Back to top

Bibliographic Information

Back to top

Publish with us

Policies and ethics

Back to top
Access via your institution

Buy it now

eBook USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Other ways to access

Search

Navigation