Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and Genetic Algorithms

✍ Scribed by Hideyuki Takagi (auth.), Da Ruan (eds.)

• Publisher: Springer US
• Year: 1997
• Tongue: English
• Leaves: 363
• Edition: 1
• Category: Library

✦ Synopsis

Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and GeneticAlgorithms is an organized edited collection of contributed chapters covering basic principles, methodologies, and applications of fuzzy systems, neural networks and genetic algorithms. All chapters are original contributions by leading researchers written exclusively for this volume.
This book reviews important concepts and models, and focuses on specific methodologies common to fuzzy systems, neural networks and evolutionary computation. The emphasis is on development of cooperative models of hybrid systems. Included are applications related to intelligent data analysis, process analysis, intelligent adaptive information systems, systems identification, nonlinear systems, power and water system design, and many others.
Intelligent Hybrid Systems: Fuzzy Logic, Neural Networks, and GeneticAlgorithms provides researchers and engineers with up-to-date coverage of new results, methodologies and applications for building intelligent systems capable of solving large-scale problems.

✦ Table of Contents

Front Matter....Pages i-xix
Front Matter....Pages 1-1
Introduction to Fuzzy Systems, Neural Networks, and Genetic Algorithms....Pages 3-33
A Fuzzy Neural Network for Approximate Fuzzy Reasoning....Pages 35-58
Novel Neural Algorithms for Solving Fuzzy Relation Equations....Pages 59-89
Methods for Simplification of Fuzzy Models....Pages 91-108
A New Approach of Neurofuzzy Learning Algorithm....Pages 109-129
Front Matter....Pages 131-131
Neural Networks in Intelligent Data Analysis....Pages 133-160
Data-Driven Identification of Key Variables....Pages 161-187
Applications of Intelligent Techniques in Process Analysis....Pages 189-208
Neurofuzzy-Chaos Engineering for Building Intelligent Adaptive Information Systems....Pages 209-230
A Sequential Training Strategy for Locally Recurrent Neural Networks....Pages 231-252
Front Matter....Pages 253-253
Adaptive Genetic Programming for System Identification....Pages 255-281
Nonlinear System Identification with Neurofuzzy Methods....Pages 283-310
A Genetic Algorithm for Mixed-Integer Optimisation in Power and Water System Design and Control....Pages 311-330
Soft Computing Based Signal Prediction, Restoration, and Filtering....Pages 331-351
Back Matter....Pages 353-354

✦ Subjects

Mathematical Logic and Foundations;Statistical Physics, Dynamical Systems and Complexity;Artificial Intelligence (incl. Robotics)