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Predictive Modular Neural Networks

Applications to Time Series

  • Vassilios Petridis
  • Athanasios Kehagias

Table of contents

  1. Front Matter
    Pages i-xi
  2. Introduction

    1. Vassilios Petridis, Athanasios Kehagias
      Pages 1-7
  3. Known Sources

    1. Front Matter
      Pages 9-9
    2. Vassilios Petridis, Athanasios Kehagias
      Pages 11-38
    3. Vassilios Petridis, Athanasios Kehagias
      Pages 39-57
    4. Vassilios Petridis, Athanasios Kehagias
      Pages 59-80
    5. Vassilios Petridis, Athanasios Kehagias
      Pages 81-97
  4. Applications

    1. Front Matter
      Pages 99-99
    2. Vassilios Petridis, Athanasios Kehagias
      Pages 101-107
    3. Vassilios Petridis, Athanasios Kehagias
      Pages 109-122
    4. Vassilios Petridis, Athanasios Kehagias
      Pages 123-133
    5. Vassilios Petridis, Athanasios Kehagias
      Pages 135-145
  5. Unknown Sources

    1. Front Matter
      Pages 147-147
    2. Vassilios Petridis, Athanasios Kehagias
      Pages 149-172
    3. Vassilios Petridis, Athanasios Kehagias
      Pages 173-207
    4. Vassilios Petridis, Athanasios Kehagias
      Pages 209-245
  6. Connections

    1. Front Matter
      Pages 247-247
    2. Vassilios Petridis, Athanasios Kehagias
      Pages 249-266
    3. Vassilios Petridis, Athanasios Kehagias
      Pages 267-269
  7. Back Matter
    Pages 271-314

About this book

Introduction

The subject of this book is predictive modular neural networks and their ap­ plication to time series problems: classification, prediction and identification. The intended audience is researchers and graduate students in the fields of neural networks, computer science, statistical pattern recognition, statistics, control theory and econometrics. Biologists, neurophysiologists and medical engineers may also find this book interesting. In the last decade the neural networks community has shown intense interest in both modular methods and time series problems. Similar interest has been expressed for many years in other fields as well, most notably in statistics, control theory, econometrics etc. There is a considerable overlap (not always recognized) of ideas and methods between these fields. Modular neural networks come by many other names, for instance multiple models, local models and mixtures of experts. The basic idea is to independently develop several "subnetworks" (modules), which may perform the same or re­ lated tasks, and then use an "appropriate" method for combining the outputs of the subnetworks. Some of the expected advantages of this approach (when compared with the use of "lumped" or "monolithic" networks) are: superior performance, reduced development time and greater flexibility. For instance, if a module is removed from the network and replaced by a new module (which may perform the same task more efficiently), it should not be necessary to retrain the aggregate network.

Keywords

algorithms artificial intelligence classification computer control engineering convergence data mining design network neural networks time series analysis

Authors and affiliations

  • Vassilios Petridis
    • 1
  • Athanasios Kehagias
    • 2
  1. 1.Aristotle University of ThessalonikiGreece
  2. 2.American College of Thessaloniki and Aristotle University of ThessalonikiGreece

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4615-5555-1
  • Copyright Information Kluwer Academic Publishers 1998
  • Publisher Name Springer, Boston, MA
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4613-7540-1
  • Online ISBN 978-1-4615-5555-1
  • Series Print ISSN 0893-3405
  • Buy this book on publisher's site