Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control

1. Front Matter

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2. Intelligent Control

   1. Front Matter

      Pages 1-1

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   2. Optimization of Membership Functions of a Fuzzy Logic Controller for an Autonomous Wheeled Mobile Robot Using Ant Colony Optimization

      • Ricardo Martínez-Marroquín, Oscar Castillo, José Soria

      Pages 3-16

   3. Evolutionary Optimization of Type-2 Fuzzy Logic Systems Applied to Linear Plants

      • Ricardo Martinez, Oscar Castillo, Luis T. Aguilar, Antonio Rodriguez

      Pages 17-31

   4. Multi-Agent System with Fuzzy Logic Control for Autonomous Mobile Robots in Known Environments

      • Cinthya Solano-Aragón, Arnulfo Alanis

      Pages 33-52

   5. Hybrid Interval Type-1 Non-singleton Type-2 Fuzzy Logic Systems Are Type-2 Adaptive Neuro-fuzzy Inference Systems

      • Gerardo M. Mendez, Ma. De Los Angeles Hernandez

      Pages 53-61

   6. Centralized Direct and Indirect Neural Control of Distributed Parameter Systems

      • Ieroham S. Baruch, Rosalba Galvan-Guerra

      Pages 63-81

3. Pattern Recognition

   1. Front Matter

      Pages 83-83

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   2. An Ensemble Neural Network Architecture with Fuzzy Response Integration for Complex Time Series Prediction

      • Martha Pulido, Alejandra Mancilla, Patricia Melin

      Pages 85-110

   3. Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms: The Case of Face, Fingerprint and Voice Recognition

      • Ricardo Muñoz, Oscar Castillo, Patricia Melin

      Pages 111-129

   4. Modular Neural Network with Fuzzy Integration of Responses for Face Recognition

      • Erika Ayala, Miguel Lopez, Patricia Melin

      Pages 131-158

   5. A Modular Neural Network with Fuzzy Response Integration for Person Identification Using Biometric Measures

      • Magdalena Serrano, Patricia Melin

      Pages 159-183

   6. Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration

      • Mónica Beltrán, Patricia Melin, Leonardo Trujillo

      Pages 185-201

4. Learning and Social Simulation

   1. Front Matter

      Pages 203-203

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   2. A Hybrid Recommender System Architecture for Learning Objects

      • Mario García-Valdez, Brunett Parra

      Pages 205-211

   3. TA-Fuzzy Semantic Networks for Interaction Representation in Social Simulation

      • Dora-Luz Flores, Antonio Rodríguez-Díaz, Juan R. Castro, Carelia Gaxiola

      Pages 213-225

   4. Fuzzy Personality Model Based on Transactional Analysis and VSM for Socially Intelligent Agents and Robots

      • Carelia Gaxiola, Antonio Rodríguez-Díaz, Susan Jones, Manuel Castañón-Puga, Dora-Luz Flores

      Pages 227-241

5. Robotics and Hardware Implementations

   1. Front Matter

      Pages 243-243

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   2. Controlling Unstable Non-Minimum-Phase Systems with Fuzzy Logic: The Perturbed Case

      • Nohe R. Cazarez-Castro, Luis T. Aguilar, Oscar Castillo, Antonio Rodríguez-Dŕaz

      Pages 245-257

   3. Genetic Optimization for the Design of Walking Patterns of a Biped Robot

      • Selene L. Cardenas-Maciel, Oscar Castillo, Luis T. Aguilar, Antonio Rodríguez-Díaz

      Pages 259-271

We describe in this book, new methods for evolutionary design of intelligent s- tems using soft computing and their applications in modeling, simulation and c- trol. Soft Computing (SC) consists of several intelligent computing paradigms, including fuzzy logic, neural networks, and evolutionary algorithms, which can be used to produce powerful hybrid intelligent systems. The book is organized in four main parts, which contain a group of papers around a similar subject. The first part consists of papers with the main theme of evolutionary design of fuzzy systems in intelligent control, which consists of papers that propose new methods for designing and optimizing intelligent controllers for different applications. The second part c- tains papers with the main theme of evolutionary design of intelligent systems for pattern recognition applications, which are basically papers using evolutionary al- rithms for optimizing modular neural networks with fuzzy systems for response - tegration, for achieving pattern recognition in different applications. The third part contains papers with the themes of models for learning and social simulation, which are papers that apply intelligent systems to the problems of designing learning - jects and social agents. The fourth part contains papers that deal with intelligent s- tems in robotics applications and hardware implementations. In the part of Intelligent Control there are 5 papers that describe different c- tributions on evolutionary optimization of fuzzy systems in intelligent control. The first paper, by Ricardo Martinez-Marroquin et al.

• agents
• algorithms
• cognition
• control
• evolution
• fuzzy
• fuzzy logic
• learning
• modeling
• neural network
• neural networks
• pattern recognition
• robot
• robotics
• simulation

• Department of Computer Science, Tijuana Institute of Technology, Chula Vista, USA

  Oscar Castillo

• Dept. Electrical and Computer Engineering, University of Alberta, Edmonton, Canada

  Witold Pedrycz

• Systems Research Institute, Polish Academy of Sciences, Warszawa, Poland

  Janusz Kacprzyk

Policies and ethics