Hybrid self-organizing modeling systems
Author(s)
Bibliographic Information
Hybrid self-organizing modeling systems
(Studies in computational intelligence, v. 211)
Springer, c2009
Available at 3 libraries
  Aomori
  Iwate
  Miyagi
  Akita
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  Fukushima
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  Tochigi
  Gunma
  Saitama
  Chiba
  Tokyo
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  Niigata
  Toyama
  Ishikawa
  Fukui
  Yamanashi
  Nagano
  Gifu
  Shizuoka
  Aichi
  Mie
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  Kyoto
  Osaka
  Hyogo
  Nara
  Wakayama
  Tottori
  Shimane
  Okayama
  Hiroshima
  Yamaguchi
  Tokushima
  Kagawa
  Ehime
  Kochi
  Fukuoka
  Saga
  Nagasaki
  Kumamoto
  Oita
  Miyazaki
  Kagoshima
  Okinawa
  Korea
  China
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  United Kingdom
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  France
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Note
Includes bibliographical references
Description and Table of Contents
Description
Models form the basis of any decision. They are used in di?erent context and for di?erent purposes: for identi?cation, prediction, classi?cation, or control of complex systems. Modeling is done theory-driven by logical-mathematical methods or data-driven based on observational data of the system and some algorithm or software for analyzing this data. Today, this approach is s- marized as Data Mining. There are many Data Mining algorithms known like Arti?cial Neural N- works, Bayesian Networks, Decision Trees, Support Vector Machines. This book focuses on another method: the Group Method of Data Handling. - thoughthismethodologyhasnotyetbeenwellrecognizedintheinternational science community asa verypowerfulmathematicalmodeling andknowledge extraction technology, it has a long history. Developed in 1968bythe Ukrainianscientist A.G. Ivakhnenko it combines the black-box approach and the connectionism of Arti?cial Neural Networks with well-proven Statistical Learning methods and with more behavior- justi?ed elements of inductive self-organization.Over the past 40 years it has been improving and evolving, ?rst by works in the ?eld of what was known in the U.S.A. as Adaptive Learning Networks in the 1970s and 1980s and later by signi?
cantcontributions from scientists from Japan,China, Ukraine, Germany. Many papers and books have been published on this modeling technology, the vast majority of them in Ukrainian and Russian language.
Table of Contents
Hybrid Computational Intelligence and GMDH Systems.- Hybrid Genetic Programming and GMDH System: STROGANOFF.- Hybrid Genetic Algorithm and GMDH System.- Hybrid Differential Evolution and GMDH Systems.- Hybrid Particle Swarm Optimization and GMDH System.- GAME - Hybrid Self-Organizing Modeling System Based on GMDH.
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