Computational Intelligence Paradigms

Innovative Applications

  • Editors
  • Lakhmi C. Jain
  • Mika Sato-Ilic
  • Maria Virvou
  • George A. Tsihrintzis
  • Valentina Emilia Balas
  • Canicious Abeynayake

Part of the Studies in Computational Intelligence book series (SCI, volume 137)

Table of contents

  1. Front Matter
  2. Lakhmi C. Jain, Shing Chiang Tan, Chee Peng Lim
    Pages 1-23
  3. Elisa Ricci, Renzo Perfetti
    Pages 109-132
  4. Terry Windeatt
    Pages 133-147
  5. Nobuo Shimizu, Masahiro Mizuta
    Pages 149-165
  6. Frank Höppner, Frank Klawonn
    Pages 167-180
  7. Ravi Jain, Andy Koronios
    Pages 181-193
  8. Mika Sato-Ilic
    Pages 195-217
  9. Rui Araújo, Urbano Nunes, Luciano Oliveira, Pedro Sousa, Paulo Peixoto
    Pages 219-250
  10. Miwako Tsuji, Masaharu Munetomo
    Pages 251-279
  11. Back Matter

About this book

Introduction

System designers are faced with a large set of data which has to be analysed and processed efficiently. Advanced computational intelligence paradigms present tremendous advantages by offering capabilities such as learning, generalisation and robustness. These capabilities help in designing complex systems which are intelligent and robust.

The book includes a sample of research on the innovative applications of advanced computational intelligence paradigms. The characteristics of computational intelligence paradigms such as learning, generalization based on learned knowledge, knowledge extraction from imprecise and incomplete data are the extremely important for the implementation of intelligent machines. The chapters include architectures of computational intelligence paradigms, knowledge discovery, pattern classification, clusters, support vector machines and gene linkage analysis. We believe that the research on computational intelligence will simulate great interest among designers and researchers of complex systems. It is important to use the fusion of various constituents of computational intelligence to offset the demerits of one paradigm by the merits of another.

Keywords

algorithm algorithms architecture calculus classification cluster analysis complex systems computational intelligence fuzzy genetic algorithms intelligence knowledge knowledge discovery learning linear optimization

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-540-79474-5
  • Copyright Information Springer-Verlag Berlin Heidelberg 2008
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-540-79473-8
  • Online ISBN 978-3-540-79474-5
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • About this book