GMDH-Methodology and Implementation in C

304 Pages·2014·6.21 MB·English

by  Godfrey Onwubolu (ed.)

GMDH
-METHODOLOGY
AND IMPLEMENTATION IN C
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GMDH
-
METHODOLOGY
AND IMPLEMENTATION IN C
Editor
Godfrey Onwubolu
Sheridan Institute of Technology & Applied Learning
Canada
Imperial College Press
ICP
P736_9781848166103_tp.indd 2 2/10/14 2:12 pm
Published by
Imperial College Press
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British Library Cataloguing-in-Publication Data
A catalogue record for this book is available from the British Library.
GMDH-METHODOLOGY AND IMPLEMENTATION IN C
(With CD-ROM)
Copyright © 2015 by Imperial College Press
All rights reserved. This book, or parts thereof, may not be reproduced in any form or by any means,
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ISBN 978-1-84816-610-3
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Printed in Singapore
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Preface
The group method of data handling (GMDH), which Ivakhnenko intro-
duced, is a typical inductive modeling method that is built on the princi-
plesofself-organization.Sinceitsintroduction,inductivemodelinghasbeen
developingandappliedtocomplexsystemsinseveralkeyareassuchaspre-
diction,modeling,clusterization,systemidentification,aswellasdatamin-
ing and knowledge extraction technologies, to several fields such as social
science,science,engineering,medicine,etc.Sinceitsintroduction,attempts
have been made to publicize the theory,algorithms,applications,solutions
andnewdevelopmentsofGMDH.AdedicatedwebsiteonGMDHisperhaps
the most useful resource center available to researchers and practitioners
to find published papers and published computer codes (www.gmdh.net).
However, many end-users who have visited this website would have been
disappointed because most of the codes are not in a form that could be
used without extensively debugging them. In general, it is extremely dif-
ficult to find error-free codes on GMDH available for use by researchers
and practitioners. Somewhere around 2008 and 2009, a major effort was
undertaken to have a central resource base detailing free GMDH codes
(http://opengmdh.org/trac/);thiswasveryusefulbutwaslaterwithdrawn
by the service providers. Consequently, although GMDH is known to be a
powerful inductive modeling method compared with an artificial neural
network(ANN), the lackofavailabilityofready-to-use,error-freecodesfor
GMDH has resulted in this subject remaining relatively unknown to many
students, researchers and practitioners.
The main purpose of this book is to fill this gap by making error-free
codes available to end-users so that these codes can be used to understand
the implementation of GMDH, and then create opportunities for further
developments of the variants of GMDH algorithms. The C language has
been chosen because it is a basic language commonly taught in computer
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vi Preface
programming in most universities and colleges, and the compiled versions
can be used for more meaningful practical applications where security is
necessary. Error-free source codes for this powerful modeling technique,
which can be modified to suit your practical needs, are available from this
book’s accompanying CD free of charge.
At the time of publishing this book on GMDH, a commercial GMDH
provider known as GMDH Shell (GS) has emerged.
Organization of the Chapters
In Chapter 1 an overview of the book in the context of the fundamentals
of GMDH is presented.
Chapter2presentsthebasicGMDHmultilayeredalgorithm(wellknown
asGMDH-MIA),whichisthemostcommonvariantusedinpractice.Chap-
ter 3 presents the GMDH multilayered algorithm using prior information;
thisisausefulphenomenonsinceapriori informationisutilizedtoenhance
the performance of GMDH-MIA. Chapter 4 presents the GMDH combina-
torial algorithm (well known as GMDH-COMBI), which has the limita-
tion that only problems with a small number of variables can be handled
using this algorithm. Chapter 5 presents the GMDH harmonic algorithm
for handling oscillatory processes. These variants of GMDH are generally
knownasparametricmethods.Chapter6dealswiththe polynomialneural
network (PNN) algorithm. The non-parametric GMDH variants are dis-
cussed in Chapters 7 and 8. Chapter 7 deals with the GMDH objective
clusteranalysis(OCA)algorithm.Amultiagent(MA)clusteringalgorithm
ispresentedinChapter8.Finally,fortimeseriesforecastingandsequential
patterns recognition, Chapter 9 covers the GMDH analogues complexing
(AC) algorithm.
Overall,Chapters2–6presentvariantsofparametricGMDHformodel-
ing;Chapter7discussesnon-parametricGMDHforclustering,whileChap-
ter 8 focuses on the multiagent clustering algorithm; and in Chapter 9,
non-parametric GMDH for time series forecasting and sequential patterns
recognitionisdiscussed.Chapter10coversthehybridofGMDH-GA,which
is genetic algorithm-based for solving different classes of problems.
Insummary,this bookpresentsanoverview ofthe GMDHvariantsand
concentrates on making availableworkable (error-free) codes in C or C++
for end-users who are ready to use these codes to solve real-life problems.
Godfrey C. Onwubolu Toronto, Canada
Editor March 2014
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Preface vii
Intended Audience
This book provides instructional material for senior undergraduates and
entry-point graduate students in computer science, cybernetics, applied
mathematics, statistics, engineering and bioinformatics. Additionally, this
book is recommended for those who are working in the areas of machine
learning,artificialintelligence,complexsystemmodelingandanalysis,neu-
ral networks and optimization. Researchers who want to know about the
fundamentals of classical GMDH-based modeling approaches will find this
book very useful as a starting point. Practitioners will also find the book
beneficial as it provides materials for those who want to apply methods
that work on real-life problems to their challenging applications.
Resources for Readers
Source codes in C language for Chapters 2–7 and 9 are enclosed in an
accompanying CD-ROM to the book; Chapters 8 and 10 are written in a
different programming language. All codes were checked that they are in
working condition before a decision was made for inclusion in the book.
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About the Editor
Dr.GodfreyOnwubolucurrentlyteachesandresearchesincomputer-aided
design (CAD) using SolidWorks, additive manufacturing (3D printing) as
well as inductive modelling and applies these technologies to industries
in Toronto, Canada. He holds a BEng degree in mechanical engineering,
and both an MSc and PhD from Aston University, Birmingham, England,
where he first developed a geometric modeling system for his graduate
studies. He worked in a number of manufacturing companies in the West
Midlands, England, and he was a professor of manufacturing engineering,
having taught courses in design and manufacturing for several years.
He has published several books with international publishing compa-
nies, such as Imperial College Press, Elsevier, and Springer-Verlag, and
has published over 130 articles in international journals. He is an active
Senior Member of both the American Society of Manufacturing Engineers
(ASMfgE) and the American Institute of Industrial Engineers (IIE).
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