Artificial Neural Networks : A Practical Course

309 Pages·2017·15.56 MB·English

by  Ivan Nunes da Silva

Ivan Nunes da Silva · Danilo Hernane Spatti
Rogerio Andrade Flauzino
Luisa Helena Bartocci Liboni
Silas Franco dos Reis Alves
Artificial Neural
Networks
A Practical Course
fi
Arti cial Neural Networks
Ivan Nunes da Silva Danilo Hernane Spatti
(cid:129)
Rogerio Andrade Flauzino
Luisa Helena Bartocci Liboni
Silas Franco dos Reis Alves
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Arti cial Neural Networks
A Practical Course
123
IvanNunes daSilva LuisaHelena Bartocci Liboni
Department ofElectrical andComputer Department ofElectrical andComputer
Engineering, USP/EESC/SEL Engineering, USP/EESC/SEL
University of SãoPaulo University of SãoPaulo
São Carlos, São Paulo São Carlos, São Paulo
Brazil Brazil
DaniloHernane Spatti Silas Francodos Reis Alves
Department ofElectrical andComputer Department ofElectrical andComputer
Engineering, USP/EESC/SEL Engineering, USP/EESC/SEL
University of SãoPaulo University of SãoPaulo
São Carlos, São Paulo São Carlos, São Paulo
Brazil Brazil
RogerioAndrade Flauzino
Department ofElectrical andComputer
Engineering, USP/EESC/SEL
University of SãoPaulo
São Carlos, São Paulo
Brazil
ISBN978-3-319-43161-1 ISBN978-3-319-43162-8 (eBook)
DOI 10.1007/978-3-319-43162-8
LibraryofCongressControlNumber:2016947754
©SpringerInternationalPublishingSwitzerland2017
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To Sileide, Karla, Solange,
Raphael and Elen
Preface
Whatareartificialneuralnetworks?Whatistheirpurpose?Whataretheirpotential
practical applications? What kind of problems can they solve?
Withthesequestionsinmind,thisbookwaswrittenwiththeprimaryconcernof
answering readers with different profiles, from those interested in acquiring
knowledgeaboutarchitecturesofartificialneuralnetworktothosemotivatedbyits
multiple applications (of practical aspect) for solving real-world problems.
Thisbookaudienceismultidisciplinary,asitwillbeconfirmedbythenumerous
exercisesandexamplesaddressedhere.Itexploresdifferentknowledgeareas,such
as engineering, computer science, mathematics, physics, economics, finance,
statistics, and neurosciences. Additionally, it is expected that this book could be
interestingfor those from many other areas that have been inthe focus ofartificial
neural networks, such as medicine, psychology, chemistry, pharmaceutical sci-
ences, biology, ecology, geology, and so on.
Regarding the academic approach of this book and its audience, the chapters
were tailored in a fashion that attempts to discuss, step-by-step, the thematic con-
cepts, covering a broad range of technical and theoretical information. Therefore,
besidesmeetingtheprofessionalaudience’sdesiretobeginordeepentheirstudyon
artificial neural networks and its potential applications, this book is intended to be
used as a textbook for undergraduate and graduate courses, which address the
subject of artificial neural networks in their syllabus.
Furthermore,thetextwascomposedusinganaccessiblelanguagesoitcouldbe
readbyprofessionals,students,researchers,andautodidactics,asastraightforward
andindependentguideforlearningbasicandadvancedsubjectsrelated toartificial
neuralnetworks.Tothisend,theprerequisitesforunderstandingthisbook’scontent
are basic, requiring only a few elementary knowledge about linear algebra, algo-
rithms, and calculus.
The first part of this book (Chaps. 1–10), which is intended for those readers
who want to begin or improve their theoretical investigation on artificial neural
networks, addresses the fundamental architectures that can be implemented in
several application scenarios.
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viii Preface
Thesecondpartofthisbook(Chaps. 11–20)wasparticularlycreatedtopresent
solutions that comprise artificial neural networks for solving practical problems
from different knowledge areas. It describes several developing details considered
toachievethedescribedresults.Suchaspectcontributestomatureandimprovethe
reader’s knowledge about the techniques of specifying the most appropriated
artificial neural network architecture for a given application.
São Carlos, Brazil Ivan Nunes da Silva
Danilo Hernane Spatti
Rogerio Andrade Flauzino
Luisa Helena Bartocci Liboni
Silas Franco dos Reis Alves
Organization
This book was carefully created with the mission of presenting an objective,
friendly, accessible,andillustrative text,whosefundamental concern is, infact, its
didactic format. The book organization, made in two parts, along with its com-
position filled with more than 200 figures, eases the knowledge building for dif-
ferentreaders’profiles.Thebibliography,composedofmorethan170references,is
thefoundationforthethemescoveredinthisbook.Thesubjectsarealsoconfronted
with up-to-date context.
Thefirstpartofthebook(PartI),dividedintotenchapters,coversthetheoretical
features related to the main artificial neural architectures, including Perceptron,
Adaptive Linear Element (ADALINE), Multilayer Perceptron, Radial Basis
Function (RBF), Hopfield, Kohonen, Learning Vector Quantization (LCQ),
Counter-Propagation,andAdaptiveResonanceTheory(ART).Ineachoneofthese
chapters from Part I, a section with exercises was inserted, so the reader can
progressively evaluate the knowledge acquired within the numerous subjects
addressed in this book.
Furthermore, the chapters that address the different architectures of neural net-
worksarealsoprovidedwithsectionsthatdiscusshands-onprojects,whosecontent
assiststhereaderonexperimentalaspectsconcerningtheproblemsthatuseartificial
neural networks. Such activities also contribute to the development of practical
knowledge, which may help on specifying, parameterizing, and tuning neural
architectures.
The second part (Part II) addresses several applications, covering different
knowledge areas, whose solutions and implementations come from the neural
networkarchitecturesexploredinPartI.Thedifferentapplicationsaddressedonthis
partofthebookreflectthepotentialapplicabilityofneuralnetworkstothesolution
of problems from engineering and applied sciences. These applications intend to
drive the reader through different modeling and mapping strategies based on arti-
ficial neural networks for solving problems of diverse nature.
ix
x Organization
Several didactic materials related to this book, including figures, exercise tips,
anddatasets for training neural networks (in tableformat), arealso available atthe
following Website:
http://laips.sel.eesc.usp.br/
In summary, this book will hopefully create a pleasant and enjoyable reading,
thus contributing to the development of a wide range of both theoretical and
practical knowledge concerning the area of artificial neural networks.
Acknowledgments
The authorsare immenselygrateful to themany colleagues who contributed tothe
realizationof this compilation,providing precioussuggestions tohelp us promptly
on this important and noble work.
In particular, we would like to express our thanks to the following colleagues:
Alexandre C.N. de Oliveira, Anderson da Silva Soares, André L.V. da Silva,
AntonioV.Ortega,DéboraM.B.S.deSouza,EdisonA.Goes,EdnaldoJ.Ferreira,
Eduardo A. Speranza, Fabiana C. Bertoni, José Alfredo C. Ulson, Juliano C.
Miranda, Lucia Valéria R. de Arruda, Marcelo Suetake, Matheus G. Pires,
Michelle M. Mendonça, Ricarco A.L. Rabêlo, Valmir Ziolkowski, Wagner C.
Amaral, Washington L.B. Melo and Wesley F. Usida.
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