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

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￾ments and advances in the various areas of computational intelligence—quickly and

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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

Janusz Kacprzyk

Editors

Design of Intelligent Systems

Based on Fuzzy Logic,

Neural Networks

and Nature-Inspired

Optimization

123

Editors

Patricia Melin

Division of Graduate Studies and Research

Tijuana Institute of Technology

Tijuana, Baja California

Mexico

Oscar Castillo

Division of Graduate Studies and Research

Tijuana Institute of Technology

Tijuana, Baja California

Mexico

Janusz Kacprzyk

Polish Academy of Sciences

Systems Research Institute

Warsaw

Poland

ISSN 1860-949X ISSN 1860-9503 (electronic)

Studies in Computational Intelligence

ISBN 978-3-319-17746-5 ISBN 978-3-319-17747-2 (eBook)

DOI 10.1007/978-3-319-17747-2

Library of Congress Control Number: 2015939806

Springer Cham Heidelberg New York Dordrecht London

© Springer International Publishing Switzerland 2015

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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￾nized in eight main parts, which contain a group of papers around a similar subject.

The first part consists of papers with the main theme of theoretical aspects of fuzzy

logic, which basically consists of papers that propose new concepts and algorithms

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￾tions of neural networks in diverse areas, such as time series prediction and pattern

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 of fuzzy

logic in diverse areas, such as time series prediction and pattern recognition.

Finally, the eighth part contains papers that present enhancements to meta-heuristics

based on fuzzy logic techniques.

In the first part of theoretical aspects of fuzzy logic, there are five papers that

describe different contributions that propose new models, concepts, and algorithms

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

different contributions that propose new models, concepts, and algorithms centered

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

describe different contributions on the application of these kinds of neural models to

solve complex real-world problems, such as time series prediction, medical diag￾nosis, and pattern recognition.

v

In the fourth part of nature-inspired optimization, there are six papers that

describe different contributions that propose new models, concepts, and algorithms

for optimization inspired in different paradigms of natural phenomena. The aim of

using these algorithms is to provide optimization capabilities to intelligent 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

that propose new models and concepts, which can be the considered as the basis for

enhancing nature-inspired algorithms with fuzzy logic. The aim of using fuzzy logic

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, particle swarm optimization, the bat algorithm, as well as new nature-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,

time series prediction, and optimization of complex problems. There are theoretical

aspects as well as application papers.

January 21, 2015 Patricia Melin

Oscar Castillo

Janusz Kacpzryk

vi Preface

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

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

viii Contents

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

Contents ix

Parallel Evolutionary Artificial Potential Field for Path

Planning—An Implementation on GPU . . . . . . . . . . . . . . . . . . . . . . . 319

Ulises Orozco-Rosas, Oscar Montiel and Roberto Sepúlveda

Design and Acceleration of a Quantum Genetic Algorithm

Through the Matlab GPU Library . . . . . . . . . . . . . . . . . . . . . . . . . . . 333

Oscar Montiel, Ajelet Rivera and Roberto Sepúlveda

Implementing Pool-Based Evolutionary Algorithm in Amazon

Cloud Computing Services. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 347

Rene Márquez Valenzuela and Mario García Valdez

An Ant Colony Algorithm for Solving the Selection Portfolio

Problem, Using a Quality-Assessment Model for Portfolios

of Projects Expressed by a Priority Ranking. . . . . . . . . . . . . . . . . . . . 357

S. Samantha Bastiani, Laura Cruz-Reyes, Eduardo Fernandez,

Claudia Gómez and Gilberto Rivera

Part VI Optimization: Theory and Applications

A Comparison Between Memetic Algorithm and Seeded Genetic

Algorithm for Multi-objective Independent Task Scheduling

on Heterogeneous Machines. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 377

Héctor Joaquín Fraire Huacuja, Alejandro Santiago, Johnatan E. Pecero,

Bernabé Dorronsoro, Pascal Bouvry, José Carlos Soto Monterrubio,

Juan Javier Gonzalez Barbosa and Claudia Gómez Santillan

Parallel Meta-heuristic Approaches to the Course

Timetabling Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 391

A. Jorge Soria-Alcaraz, Martin Carpio, Hector Puga, Jerry Swan,

Patricia Melin, Hugo Terashima and A. Marco Sotelo-Figueroa

Simplification of Decision Rules for Recommendation of Projects

in a Public Project Portfolio. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 419

Laura Cruz-Reyes, César Medina Trejo, Fernando López Irrarragorri

and Claudia G. Gómez Santillan

A Survey of Grey Systems Applied to Multi-objective Problem . . . . . . 431

Fausto Balderas, Eduardo Fernandez, Claudia Gómez

and Laura Cruz-Reyes

x Contents

An Efficient Representation Scheme of Candidate Solutions

for the Master Bay Planning Problem . . . . . . . . . . . . . . . . . . . . . . . . 441

Paula Hernández Hernández, Laura Cruz-Reyes, Patricia Melin,

Julio Mar-Ortiz, Héctor Joaquín Fraire Huacuja,

Héctor José Puga Soberanes and Juan Javier González Barbosa

Verifying the Effectiveness of an Evolutionary Approach in Solving

Many-Objective Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . 455

Laura Cruz-Reyes, Eduardo Fernandez, Claudia Gomez,

Patricia Sanchez, Guadalupe Castilla and Daniel Martinez

Comparative Study on Constructive Heuristics for the Vertex

Separation Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 465

Norberto Castillo-García, Héctor Joaquín Fraire Huacuja,

José Antonio Martínez Flores, Rodolfo A. Pazos Rangel,

Juan Javier González Barbosa and Juan Martín Carpio Valadez

Part VII Fuzzy Logic Applications

A New Approach for Intelligent Control of Nonlinear Dynamic

Plants Using a Benchmark Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 477

Leticia Cervantes and Oscar Castillo

Fuzzy Pre-condition Rules for Activity Sequencing

in Intelligent Learning Environments . . . . . . . . . . . . . . . . . . . . . . . . . 489

Francisco Arce and Mario García-Valdez

A Pre-filtering Based Context-Aware Recommender System

using Fuzzy Rules . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 497

Xochilt Ramirez-Garcia and Mario Garcia-Valdez

A Fitness Estimation Strategy for Web Based Interactive

Evolutionary Applications Considering User Preferences

and Activities Using Fuzzy Logic . . . . . . . . . . . . . . . . . . . . . . . . . . . . 507

J.C. Romero and M. García-Valdez

Design of a Fuzzy System for Diagnosis of Hypertension . . . . . . . . . . . 517

Juan Carlos Guzmán, Patricia Melin and German Prado-Arechiga

Trajectory Metaheuristics for the Internet Shopping

Optimization Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 527

Mario C. López-Locés, Kavita Rege, Johnatan E. Pecero,

Pascal Bouvry and Héctor J. Fraire Huacuja

Contents xi

Analysis of Some Database Schemas Used to Evaluate Natural

Language Interfaces to Databases. . . . . . . . . . . . . . . . . . . . . . . . . . . . 537

Rogelio Florencia-Juárez, Juan J. González B., Rodolfo A. Pazos R.,

José A. Martínez F. and María L. Morales-Rodríguez

Part VIII Fuzzy Logic and Metaheuristics

Cuckoo Search Algorithm via Lévy Flight with Dynamic Adaptation

of Parameter Using Fuzzy Logic for Benchmark

Mathematical Functions. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 555

Maribel Guerrero, Oscar Castillo and Mario García

Differential Evolution with Dynamic Adaptation of Parameters

for the Optimization of Fuzzy Controllers. . . . . . . . . . . . . . . . . . . . . . 573

Patricia Ochoa, Oscar Castillo and José Soria

Ant Colony Optimization with Parameter Adaptation

Using Fuzzy Logic for TSP Problems . . . . . . . . . . . . . . . . . . . . . . . . . 593

Frumen Olivas, Fevrier Valdez and Oscar Castillo

An Improved Harmony Search Algorithm Using Fuzzy Logic

for the Optimization of Mathematical Functions . . . . . . . . . . . . . . . . . 605

Cinthia Peraza, Fevrier Valdez and Oscar Castillo

A New Algorithm Based in the Smart Behavior of the Bees

for the Design of Mamdani-Style Fuzzy Controllers

Using Complex Non-linear Plants. . . . . . . . . . . . . . . . . . . . . . . . . . . . 617

Leticia Amador-Angulo and Oscar Castillo

xii Contents

Part I

Fuzzy Logic Theory

Color Image Edge Detection Method

Based on Interval Type-2 Fuzzy Systems

Claudia I. Gonzalez, Patricia Melin, Juan R. Castro, Olivia Mendoza

and Oscar Castillo

Abstract Edge detection is one of the most commonly used operations in com￾puter vision, image processing and pattern recognition. The efficiency of these

applications depends in many cases on the quality of detected edges. A color image

edge detection method based on Sobel and Interval type-2 fuzzy system IT2FSs is

presented in this paper. Color images provide more information than grayscale

images. Thus, more edge information is expected from a color edge detector than a

grayscale edge detector. The proposed method is applied over a database of color

images that include synthetic and real images. The performance of the proposed

method is compared with other edge detection algorithms such as Sobel combined

with type-1 fuzzy systems T1FSs and the traditional Sobel operator.

1 Introduction

Edge detection in color images is a far more difficult task than gray scale images but

the color images provide more information than grayscale images; this can be vital

in some computer vision applications [1]. Additionally, human perception of color

images is more enriched than an achromatic picture [2]. Several color models are

present such as RGB color model, YUV model, CMY color model, CMYK color

model, HIS color model [3, 4].

This paper describes the application of the color image edge detection based on

Sobel and interval type-2 fuzzy systems, which is performed using the RGB color

model. The algorithm is tested on synthetic and real images and the results are

C.I. Gonzalez
J.R. Castro
O. Mendoza

Autonomous University of Baja California, Tijuana, Mexico

P. Melin (&)
O. Castillo

Tijuana Institute of Technology, Tijuana, Mexico

e-mail: [email protected]

O. Castillo

e-mail: [email protected]

© Springer International Publishing Switzerland 2015

P. Melin et al. (eds.), Design of Intelligent Systems Based on Fuzzy Logic,

Neural Networks and Nature-Inspired Optimization,

Studies in Computational Intelligence 601, DOI 10.1007/978-3-319-17747-2_1

3