Advertisement

Hybrid Self-Organizing Modeling Systems

  • Godfrey C. Onwubolu

Part of the Studies in Computational Intelligence book series (SCI, volume 211)

Table of contents

  1. Front Matter
  2. Nader Nariman-zadeh, Jamali Ali
    Pages 99-138
  3. Godfrey Onwubolu
    Pages 139-191
  4. Anurag Sharma, Godfrey Onwubolu
    Pages 193-231
  5. Back Matter

About this book

Introduction

The Group Method of Data Handling (GMDH) is a typical inductive modeling method that is built on principles of self-organization for modeling complex systems. However, it is known to often under-perform on non-parametric regression tasks, while time series modeling GMDH exhibits a tendency to find very complex polynomials that cannot model well future, unseen oscillations of the series. In order to alleviate these problems, GMDH has been recently hybridized with some computational intelligence (CI) techniques resulting in more robust and flexible hybrid intelligent systems for solving complex, real-world problems. The central theme of this book is to present in a very clear manner hybrids of some computational intelligence techniques and GMDH approach.

The hybrids discussed in the book include GP-GMDH (Genetic Programming-GMDH) algorithm, GA-GMDH (Genetic Algorithm-GMDH) algorithm, DE-GMDH (Differential Evolution-GMDH) algorithm, and PSO-GMDH (Particle Swarm Optimization) algorithm. Also included is the description of the recently introduced GAME (Group Adaptive Models Evolution algorithm.

The hybrid character of models and their self-organizing ability give these hybrid self-organizing modeling systems an advantage over standard data mining models.

The modeling and data mining solutions of several real-life problems in the areas of engineering, bioinformatics, finance, and economics are presented in the chapters. The book will benefit amongst others, people who are working in the areas of neural networks, machine learning, artificial intelligence, complex system modeling and analysis, and optimization.

Keywords

algorithm algorithms artificial intelligence bioinformatics complex system complex systems computational intelligence data mining evolution genetic algorithms genetic programming intelligence modeling optimization programming

Editors and affiliations

  • Godfrey C. Onwubolu
    • 1
  1. 1.Knowledge Management & Mining Inc.Richmond HillCanada

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-642-01530-4
  • Copyright Information Springer Berlin Heidelberg 2009
  • Publisher Name Springer, Berlin, Heidelberg
  • eBook Packages Engineering
  • Print ISBN 978-3-642-01529-8
  • Online ISBN 978-3-642-01530-4
  • Series Print ISSN 1860-949X
  • Series Online ISSN 1860-9503
  • Buy this book on publisher's site
Industry Sectors
Materials & Steel
Automotive
Chemical Manufacturing
Biotechnology
Electronics
IT & Software
Telecommunications
Consumer Packaged Goods
Energy, Utilities & Environment
Aerospace
Oil, Gas & Geosciences
Engineering