Artificial Organic Networks: Artificial Intelligence Based on Carbon Networks

232 Pages·2014·8.88 MB·English

by  Hiram Ponce-Espinosa

Studies in Computational Intelligence 521
Hiram Ponce-Espinosa
Pedro Ponce-Cruz
Arturo Molina
Artificial Organic
Networks
Artificial Intelligence Based on
Carbon Networks
Studies in Computational Intelligence
Volume 521
Series Editor
J. Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
For furthervolumes:
http://www.springer.com/series/7092
About the Series
The series ‘‘Studies in Computational Intelligence’’ (SCI) publishes new devel-
opmentsandadvancesinthevariousareasofcomputationalintelligence–quickly
andwithahighquality.Theintentistocoverthetheory,applications,anddesign
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particualr value to both the contributors and the
readership are the short publication timeframe and the world-wide distribution,
which enable both wide and rapid dissemination of research output.
Special offer: For all clients with a print standing order we offer free access to
the electronic volumes of the Series published in the current year.
Indexed by DBLP, Ulrichs, SCOPUS, MathSciNet, Current Mathematical
Publications, Mathematical Reviews, Zentralblatt Math: MetaPress and
Springerlink.
Hiram Ponce-Espinosa
•
Pedro Ponce-Cruz Arturo Molina
•
Artificial Organic Networks
Artificial Intelligence Based on
Carbon Networks
123
Hiram Ponce-Espinosa
Pedro Ponce-Cruz
ArturoMolina
InstitutoTecnológico de Estudios
Superiores deMonterrey
Campus Ciudadde México
Tlalpan Distrito Federal
Mexico
ISSN 1860-949X ISSN 1860-9503 (electronic)
ISBN 978-3-319-02471-4 ISBN 978-3-319-02472-1 (eBook)
DOI 10.1007/978-3-319-02472-1
SpringerChamHeidelbergNewYorkDordrechtLondon
LibraryofCongressControlNumber:2013950734
(cid:2)SpringerInternationalPublishingSwitzerland2014
Thisworkissubjecttocopyright.AllrightsarereservedbythePublisher,whetherthewholeorpartof
the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations,
recitation,broadcasting,reproductiononmicrofilmsorinanyotherphysicalway,andtransmissionor
informationstorageandretrieval,electronicadaptation,computersoftware,orbysimilarordissimilar
methodology now known or hereafter developed. Exempted from this legal reservation are brief
excerpts in connection with reviews or scholarly analysis or material supplied specifically for the
purposeofbeingenteredandexecutedonacomputersystem,forexclusiveusebythepurchaserofthe
work. Duplication of this publication or parts thereof is permitted only under the provisions of
theCopyright Law of the Publisher’s location, in its current version, and permission for use must
always be obtained from Springer. Permissions for use may be obtained through RightsLink at the
CopyrightClearanceCenter.ViolationsareliabletoprosecutionundertherespectiveCopyrightLaw.
The use of general descriptive names, registered names, trademarks, service marks, etc. in this
publicationdoesnotimply,evenintheabsenceofaspecificstatement,thatsuchnamesareexempt
fromtherelevantprotectivelawsandregulationsandthereforefreeforgeneraluse.
While the advice and information in this book are believed to be true and accurate at the date of
publication,neithertheauthorsnortheeditorsnorthepublishercanacceptanylegalresponsibilityfor
anyerrorsoromissionsthatmaybemade.Thepublishermakesnowarranty,expressorimplied,with
respecttothematerialcontainedherein.
Printedonacid-freepaper
SpringerispartofSpringerScience+BusinessMedia(www.springer.com)
To Omar who makes me believe in the
unbelievable
To my parents and grandfather
—Hiram Ponce-Espinosa
To Norma, Pedro and Jamie who are always
very close to my heart
—Pedro Ponce-Cruz
To my lovely family: Silvia, Julio and
Monserrat
—Arturo Molina
Preface
This book was written for undergraduate and graduate students as well as
researchersandscientistsinterestedinartificialintelligencetechniques.Infact,the
intention of this book is to introduce and fully describe the artificial organic
networkstechnique,anovelmachinelearningmethodinspiredonchemicalcarbon
networks. In addition, an organic network-based algorithm named artificial
hydrocarbon networks is presented to show the advantages and the scope of the
technique.
On one hand, the book is complemented with several examples through
chaptersandthedescriptionofreal-worldapplicationsusingartificialhydrocarbon
networks. On the other hand, the text is accompanied with an artificial organic
networkstoolkitimplementedonLabVIEWTMallowingahands-on experienceto
readers.
Theorganizationofthebookisasfollows:Chapter 1introducesanoverviewof
machinelearningandthemodelingproblemwhileChap. 2describeskeyconcepts
oforganicchemistryinordertounderstandthetechnique,Chaps. 3and4describe
the artificial organic networks technique and the artificial hydrocarbon networks
algorithm. Then, Chap. 5 offers some improvements to the basic artificial hydro-
carbon networks algorithm. Finally, Chaps. 6 and 7 provide experimental results
and discuss how to implement the algorithm in real-world applications like audio
filtering, control systems and facial recognition.
Finally, we would like to express our gratitude to all those who provided
supportandrevieweddetailsoverandover,thosewhoreadandofferedcomments
allowed us to quote their remarks, and those who assisted us in the editing,
proofreadinganddesigningstages.AspecialacknowledgementtotheTecnologico
de Monterrey.
Mexico City, Mexico, August 2013 Hiram Ponce-Espinosa
Pedro Ponce-Cruz
Arturo Molina
vii
Contents
1 Introduction to Modeling Problems . . . . . . . . . . . . . . . . . . . . . . . 1
1.1 The Modeling Problem. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Review of Machine Learning . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2.1 Learning Algorithms. . . . . . . . . . . . . . . . . . . . . . . . . . 4
1.2.2 Classification Algorithms. . . . . . . . . . . . . . . . . . . . . . . 6
1.3 Nature-Inspired Computing. . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.3.1 Metaheuristic Algorithms. . . . . . . . . . . . . . . . . . . . . . . 8
1.3.2 Evolutionary Algorithms . . . . . . . . . . . . . . . . . . . . . . . 9
1.3.3 Biologically Inspired Algorithms . . . . . . . . . . . . . . . . . 9
1.3.4 Chemically Inspired Algorithms . . . . . . . . . . . . . . . . . . 14
1.4 Comparison of Algorithms for Modeling Problems . . . . . . . . . . 17
1.4.1 Complexity and Stability in Modeling Problems. . . . . . . 17
1.4.2 Artificial Organic Networks and Modeling Problems. . . . 20
1.5 Motivation of Artificial Organic Networks. . . . . . . . . . . . . . . . 24
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29
2 Chemical Organic Compounds. . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.1 The Importance of Organic Chemistry. . . . . . . . . . . . . . . . . . . 32
2.2 Basic Concepts of Organic Compounds . . . . . . . . . . . . . . . . . . 33
2.2.1 Structural Definitions. . . . . . . . . . . . . . . . . . . . . . . . . . 34
2.2.2 Chemical Definitions. . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.3 Covalent Bonding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
2.3.1 Characterization of Covalent Bonds . . . . . . . . . . . . . . . 40
2.4 Energy in Organic Compounds. . . . . . . . . . . . . . . . . . . . . . . . 42
2.4.1 Energy Level Scheme . . . . . . . . . . . . . . . . . . . . . . . . . 42
2.4.2 Measures of Energy . . . . . . . . . . . . . . . . . . . . . . . . . . 43
2.5 Classification of Organic Compounds . . . . . . . . . . . . . . . . . . . 45
2.5.1 Hydrocarbons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.5.2 Alcohols, Ethers, and Thiols. . . . . . . . . . . . . . . . . . . . . 46
2.5.3 Amines . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47
2.5.4 Aldehydes, Ketones, and Carboxylic Acids . . . . . . . . . . 47
2.5.5 Polymers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48
2.5.6 Carbohydrates, Lipids, Amino Acids, and Proteins . . . . . 48
2.5.7 Nucleic Acids. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
ix
x Contents
2.6 Organic Compounds as Inspiration . . . . . . . . . . . . . . . . . . . . . 49
2.6.1 Motivation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.6.2 Characteristics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52
3 Artificial Organic Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
3.1 Overview of Artificial Organic Networks. . . . . . . . . . . . . . . . . 53
3.1.1 The Metaphor. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
3.1.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55
3.2 Artificial Organic Compounds. . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.1 Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56
3.2.2 Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
3.3 Networks of Artificial Organic Compounds . . . . . . . . . . . . . . . 66
3.3.1 The Structure . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.3.2 The Behavior . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66
3.3.3 Mixtures of Compounds. . . . . . . . . . . . . . . . . . . . . . . . 66
3.4 The Technique of Artificial Organic Networks . . . . . . . . . . . . . 67
3.4.1 Levels of Energy in Components . . . . . . . . . . . . . . . . . 67
3.4.2 Formal Definition of Artificial Organic Networks. . . . . . 68
3.4.3 Model of Artificial Organic Networks. . . . . . . . . . . . . . 69
3.5 Implementation Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
3.5.1 The Search Topological Parameters Problem . . . . . . . . . 70
3.5.2 The Build Topological Structure Problem . . . . . . . . . . . 71
3.5.3 Artificial Organic Networks-Based Algorithms. . . . . . . . 72
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72
4 Artificial Hydrocarbon Networks . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.1 Introduction to Artificial Hydrocarbon Networks. . . . . . . . . . . . 73
4.1.1 Chemical Inspiration. . . . . . . . . . . . . . . . . . . . . . . . . . 73
4.1.2 Objectives and Scope . . . . . . . . . . . . . . . . . . . . . . . . . 74
4.2 Basics of Artificial Hydrocarbon Networks. . . . . . . . . . . . . . . . 75
4.2.1 Components. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75
4.2.2 Interactions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
4.2.3 The Algorithm . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
4.2.4 Mathematical Formulation . . . . . . . . . . . . . . . . . . . . . . 101
4.3 Metrics of Artificial Hydrocarbon Networks. . . . . . . . . . . . . . . 102
4.3.1 Computational Complexity. . . . . . . . . . . . . . . . . . . . . . 102
4.3.2 Stability. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105
4.4 Artificial Hydrocarbon Networks Practical Features. . . . . . . . . . 108
4.4.1 Partial Knowledge Representation. . . . . . . . . . . . . . . . . 108
4.4.2 Practical Issues in Partial Knowledge Extraction. . . . . . . 110
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
Contents xi
5 Enhancements of Artificial Hydrocarbon Networks. . . . . . . . . . . . 113
5.1 Optimization of the Number of Molecules . . . . . . . . . . . . . . . . 113
5.1.1 The Hess’ Law. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113
5.1.2 Boiling and Melting Points in Hydrocarbons . . . . . . . . . 114
5.1.3 Enthalpy in Artificial Hydrocarbon Networks. . . . . . . . . 115
5.2 Extension to the Multidimensional Case. . . . . . . . . . . . . . . . . . 119
5.2.1 Components and Interactions . . . . . . . . . . . . . . . . . . . . 120
5.2.2 Multidimensional AHN-Algorithm . . . . . . . . . . . . . . . . 124
5.3 Recursive Networks Using Aromatic Compounds . . . . . . . . . . . 127
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129
6 Notes on Modeling Problems Using Artificial Hydrocarbon
Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.1 Approximation Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 131
6.1.1 Approximation of Univariate Functions. . . . . . . . . . . . . 132
6.1.2 Approximation of Multivariate Functions. . . . . . . . . . . . 137
6.2 Clustering Problems. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.2.1 Linear Classifiers . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142
6.2.2 Nonlinear Classifiers. . . . . . . . . . . . . . . . . . . . . . . . . . 145
6.3 Guidelines for Real-World Applications. . . . . . . . . . . . . . . . . . 149
6.3.1 Inheritance of Information . . . . . . . . . . . . . . . . . . . . . . 150
6.3.2 Catalog Based on Artificial Compounds . . . . . . . . . . . . 152
6.3.3 Using Metadata. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 152
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 153
7 Applications of Artificial Hydrocarbon Networks . . . . . . . . . . . . . 155
7.1 Filtering Process in Audio Signals. . . . . . . . . . . . . . . . . . . . . . 155
7.1.1 Background and Problem Statement . . . . . . . . . . . . . . . 156
7.1.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157
7.1.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 159
7.2 Position Control of DC Motor Using AHN-Fuzzy
Inference Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 165
7.2.1 Background and Problem Statement . . . . . . . . . . . . . . . 166
7.2.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 172
7.2.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 177
7.3 Facial Recognition Based on Signal Identification
Using AHNs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 183
7.3.1 Background and Problem Statement . . . . . . . . . . . . . . . 183
7.3.2 Methodology . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
7.3.3 Results and Discussion . . . . . . . . . . . . . . . . . . . . . . . . 187
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 189