Design of Intelligent Systems Based on Fuzzy Logic, Neural Networks and Nature-Inspired Optimization

612 Pages·2015·19.21 MB·English

by  Patricia Melin

Studies in Computational Intelligence 601
Patricia Melin
Oscar Castillo
Janusz Kacprzyk Editors
Design of Intelligent
Systems Based on Fuzzy
Logic, Neural Networks
and Nature-Inspired
Optimization
Studies in Computational Intelligence
Volume 601
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
About this Series
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
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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
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More information about this series at http://www.springer.com/series/7092
Patricia Melin Oscar Castillo
(cid:129)
Janusz Kacprzyk
Editors
Design of Intelligent Systems
Based on Fuzzy Logic,
Neural Networks
and Nature-Inspired
Optimization
123
Editors
Patricia Melin JanuszKacprzyk
Division of Graduate Studies andResearch Polish Academy ofSciences
TijuanaInstitute of Technology Systems Research Institute
Tijuana, BajaCalifornia Warsaw
Mexico Poland
Oscar Castillo
Division of Graduate Studies andResearch
TijuanaInstitute of Technology
Tijuana, BajaCalifornia
Mexico
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-319-17746-5 ISBN978-3-319-17747-2 (eBook)
DOI 10.1007/978-3-319-17747-2
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Preface
We describe, in this book, recent advances on the design of intelligent systems
based on fuzzy logic, neural networks, and nature-inspired optimization and their
application in areas, such as intelligent control and robotics, pattern recognition,
time series prediction, and optimization of complex problems. The book is orga-
nizedineightmainparts,whichcontainagroupofpapersaroundasimilarsubject.
Thefirstpartconsistsofpaperswiththemainthemeoftheoreticalaspectsoffuzzy
logic,whichbasicallyconsistsofpapersthatproposenewconceptsandalgorithms
based on fuzzy systems. The second part contains papers with the main theme of
neural networks theory, which are basically papers dealing with new concepts and
algorithms in neural networks. The third part contains papers describing applica-
tionsofneuralnetworksindiverseareas,suchastimeseriespredictionandpattern
recognition. The fourth part contains papers describing new nature-inspired opti-
mization algorithms. The fifth part presents diverse applications of nature-inspired
optimization algorithms. The sixth part contains papers describing new optimiza-
tion algorithms. The seventh part contains papers describing applications offuzzy
logic in diverse areas, such as time series prediction and pattern recognition.
Finally,theeighthpartcontainspapersthatpresentenhancementstometa-heuristics
based on fuzzy logic techniques.
In the first part of theoretical aspects of fuzzy logic, there are five papers that
describedifferentcontributionsthatproposenewmodels,concepts,andalgorithms
centered on fuzzy logic. The aim of using fuzzy logic is to provide uncertainty
management in modeling complex problems.
In the second part of neural networks theory, there are five papers that describe
differentcontributionsthatproposenewmodels,concepts,andalgorithmscentered
on neural networks. The aim of using neural networks is to provide learning and
adaptive capabilities to intelligent systems.
In the third part of neural network applications, there are five papers that
describedifferentcontributionsontheapplicationofthesekindsofneuralmodelsto
solve complex real-world problems, such as time series prediction, medical diag-
nosis, and pattern recognition.
v
vi Preface
In the fourth part of nature-inspired optimization, there are six papers that
describedifferentcontributionsthatproposenewmodels,concepts,andalgorithms
for optimization inspired in different paradigms of natural phenomena. The aim of
using thesealgorithms istoprovideoptimizationcapabilities tointelligent systems
or provide design methodologies for achieving optimal topological and parametric
design of intelligent systems.
In the fifth part of nature-inspired optimization applications, there are seven
papers that describe different contributions on the application of these kinds of
algorithms to solve complex real-world optimization problems, such as time series
prediction, medical diagnosis, robotics, and pattern recognition.
In the sixth part of optimization, there are seven papers that describe different
contributions that propose new models, concepts, and algorithms for optimization
inspired in different paradigms. The aim of using these algorithms is to provide
general optimization methods and solution to some real-world problem in areas,
such as scheduling, planning, and project portfolios.
In the seventh part of fuzzy logic applications, there are seven papers that
describe different contributions on the application of these kinds of fuzzy logic
models to solve complex real-world problems, such as time series prediction,
medical diagnosis, recommending systems, education, and pattern recognition.
In the eighth part of fuzzy logic for the augmentation of nature-inspired opti-
mization meta-heuristics, there are five papers that describe different contributions
thatproposenewmodelsandconcepts,whichcanbetheconsideredasthebasisfor
enhancingnature-inspiredalgorithmswithfuzzylogic.Theaimofusingfuzzylogic
is to provide dynamic adaptation capabilities to the optimization algorithms, and
this is illustrated with the cases of the bat algorithm, cuckoo search, and other
methods. The nature-inspired methods include variations of ant colony optimiza-
tion,particleswarmoptimization,thebatalgorithm,aswellasnewnature-inspired
paradigms.
In conclusion, the edited book comprises papers on diverse aspects of fuzzy
logic, neural networks, and nature-inspired optimization meta-heuristics and their
application in areas, such as intelligent control and robotics, pattern recognition,
timeseriesprediction,andoptimizationofcomplexproblems.Therearetheoretical
aspects as well as application papers.
January 21, 2015 Patricia Melin
Oscar Castillo
Janusz Kacpzryk
Contents
Part I Fuzzy Logic Theory
Color Image Edge Detection Method Based on Interval
Type-2 Fuzzy Systems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Olivia Mendoza
and Oscar Castillo
Method for Measurement of Uncertainty Applied to the Formation
of Interval Type-2 Fuzzy Sets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13
Mauricio A. Sanchez, Oscar Castillo and Juan R. Castro
Optimization of the Interval Type-2 Fuzzy Integrators in Ensembles
of ANFIS Models for Time Series Prediction:
Case of the Mexican Stock Exchange . . . . . . . . . . . . . . . . . . . . . . . . . 27
Jesus Soto and Patricia Melin
A New Proposal for a Granular Fuzzy C-Means Algorithm. . . . . . . . . 47
Elid Rubio and Oscar Castillo
Face Recognition with a Sobel Edge Detector and the Choquet
Integral as Integration Method in a Modular Neural Networks. . . . . . 59
Gabriela E. Martínez, Patricia Melin, Olivia D. Mendoza
and Oscar Castillo
Part II Neural Networks Theory
Neural Network with Fuzzy Weights Using Type-1
and Type-2 Fuzzy Learning for the Dow-Jones Time Series. . . . . . . . . 73
Fernando Gaxiola, Patricia Melin and Fevrier Valdez
vii
viii Contents
Evolutionary Indirect Design of Feed-Forward Spiking
Neural Networks. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Andrés Espinal, Martín Carpio, Manuel Ornelas, Héctor Puga,
Patricia Melín and Marco Sotelo-Figueroa
Cellular Neural Network Scheme for Image Binarization
in Video Sequence Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 103
Mario I. Chacon-Murguia and Juan A. Ramirez-Quintana
Optimization of the LVQ Network Architecture
with a Modular Approach for Arrhythmia
Classification Using PSO . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
Jonathan Amezcua and Patricia Melin
Evolution of Kernels for Support Vector Machine Classification
on Large Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 127
Luis Carlos Padierna, Martín Carpio, Rosario Baltazar,
Héctor José Puga and Héctor Joaquín Fraire
Part III Neural Networks Applications
Modular Neural Networks for Time Series Prediction
Using Type-1 Fuzzy Logic Integration . . . . . . . . . . . . . . . . . . . . . . . . 141
Daniela Sánchez and Patricia Melin
An Improved Particle Swarm Optimization Algorithm
to Optimize Modular Neural Network Architectures. . . . . . . . . . . . . . 155
Alfonso Uriarte, Patricia Melin and Fevrier Valdez
Left Ventricular Border Recognition in Echocardiographic Images
Using Modular Neural Networks and Sugeno Integral Measures . . . . . 163
Fausto Rodríguez-Ruelas, Patricia Melin and German Prado-Arechiga
Optimization of Ensemble Neural Networks with Fuzzy Integration
Using the Particle Swarm Algorithm for Time Series Prediction . . . . . 171
Martha Pulido and Patricia Melin
A Type-2 Fuzzy Neural Network Ensemble to Predict Chaotic
Time Series. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 185
Victor M. Torres and Oscar Castillo
Contents ix
Part IV Nature Inspired Optimization
Study of Parameter Variations in the Cuckoo Search Algorithm
and the Influence in Its Behavior. . . . . . . . . . . . . . . . . . . . . . . . . . . . 199
Maribel Guerrero, Oscar Castillo and Mario García
A New Bio-inspired Optimization Algorithm Based
on the Self-defense Mechanisms of Plants. . . . . . . . . . . . . . . . . . . . . . 211
Camilo Caraveo, Fevrier Valdez and Oscar Castillo
Imperialist Competitive Algorithm Applied to the Optimization
of Mathematical Functions: A Parameter Variation Study. . . . . . . . . . 219
Emer Bernal, Oscar Castillo and José Soria
An Improved Intelligent Water Drop Algorithm to Solve
Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 233
Diana Martinez and Fevrier Valdez
An Improved Simulated Annealing Algorithm for the Optimization
of Mathematical Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 241
Carolina Avila and Fevrier Valdez
Optimization of Reactive Fuzzy Controllers for Mobile Robots
Based on the Chemical Reactions Algorithm. . . . . . . . . . . . . . . . . . . . 253
David de la O, Oscar Castillo, Abraham Meléndez, Patricia Melin,
Leslie Astudillo and Coral Sánchez
Part V Nature Inspired Optimization Applications
Segmentation of Coronary Angiograms Using a Vesselness
Measure and Evolutionary Thresholding . . . . . . . . . . . . . . . . . . . . . . 269
Ivan Cruz-Aceves and Arturo Hernández-Aguirre
Exploring the Suitability of a Genetic Algorithm
as Tool for Boosting Efficiency in Monte Carlo Estimation
of Leaf Area of Eelgrass . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 291
Cecilia Leal-Ramirez, Héctor Echavarría-Heras and Oscar Castillo
Obtaining Pharmacokinetic Population Models
Using a Genetic Algorithm Approach. . . . . . . . . . . . . . . . . . . . . . . . . 305
Oscar Montiel, J.M. Cornejo, Carlos Sepúlveda and Roberto Sepúlveda