Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control

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.

Recent results in Evolutionary Design of Intelligent Systems in Modeling, Simulation and Control

Inhalt
Intelligent Control.- Optimization of Membership Functions of a Fuzzy Logic Controller for an Autonomous Wheeled Mobile Robot Using Ant Colony Optimization.- Evolutionary Optimization of Type-2 Fuzzy Logic Systems Applied to Linear Plants.- Multi-Agent System with Fuzzy Logic Control for Autonomous Mobile Robots in Known Environments.- Hybrid Interval Type-1 Non-singleton Type-2 Fuzzy Logic Systems Are Type-2 Adaptive Neuro-fuzzy Inference Systems.- Centralized Direct and Indirect Neural Control of Distributed Parameter Systems.- Pattern Recognition.- An Ensemble Neural Network Architecture with Fuzzy Response Integration for Complex Time Series Prediction.- Optimization of Fuzzy Response Integrators in Modular Neural Networks with Hierarchical Genetic Algorithms: The Case of Face, Fingerprint and Voice Recognition.- Modular Neural Network with Fuzzy Integration of Responses for Face Recognition.- A Modular Neural Network with Fuzzy Response Integration for Person Identification Using Biometric Measures.- Signature Recognition with a Hybrid Approach Combining Modular Neural Networks and Fuzzy Logic for Response Integration.- Learning and Social Simulation.- A Hybrid Recommender System Architecture for Learning Objects.- TA-Fuzzy Semantic Networks for Interaction Representation in Social Simulation.- Fuzzy Personality Model Based on Transactional Analysis and VSM for Socially Intelligent Agents and Robots.- Robotics and Hardware Implementations.- Controlling Unstable Non-Minimum-Phase Systems with Fuzzy Logic: The Perturbed Case.- Genetic Optimization for the Design of Walking Patterns of a Biped Robot.- Design and Simulation of the Type-2 Fuzzification Stage: Using Active Membership Functions.- Methodology to Test and Validate a VHDL Inference Engine of a Type-2 FIS, through the Xilinx System Generator.- Modeling and Simulation of the Defuzzification Stage of a Type-2 Fuzzy Controller Using the Xilinx System Generator and Simulink.