Evolutionary design of intelligent systems in modeling, simulation and control /

Evolutionary design of intelligent systems in modeling, simulation and control /

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Bibliographic Details
Imprint:Berlin : Springer, ©2009.
Description:1 online resource (ix, [327] pages) : illustrations.
Language:English
Series:Studies in computational intelligence, 1860-949X ; v. 257
Studies in computational intelligence ; v. 257.
Subject:
Intelligent control systems.
Soft computing.
Fuzzy systems.
Pattern recognition systems.
Fuzzy systems.
Intelligent control systems.
Pattern recognition systems.
Soft computing.
Format: E-Resource Book
URL for this record:http://pi.lib.uchicago.edu/1001/cat/bib/11073531
Hidden Bibliographic Details
Other authors / contributors:Castillo, Oscar, 1959-
Pedrycz, Witold, 1953-
Kacprzyk, Janusz.
ISBN:9783642045141
3642045146
9783642045134
3642045138
Notes:Includes bibliographical references and index.
Print version record.
Summary:The editors describe in this book, new methods for evolutionary design of intelligent systems using soft computing and their applications in modeling, simulation and control. 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 contains papers with the main theme of evolutionary design of intelligent systems for pattern recognition applications, which are basically papers using evolutionary algorithms for optimizing modular neural networks with fuzzy systems for response integration, 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 objects and social agents. The fourth part contains papers that deal with intelligent systems in robotics applications and hardware implementations.
Other form:Print version: Evolutionary design of intelligent systems in modeling, simulation and control. Berlin : Springer, ©2009 9783642045134
Table of Contents:
  • 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.

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