Recent Advances in Interval Type-2 Fuzzy Systems, 2012 SpringerBriefs in Computational Intelligence Series

This book reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques. Combining type-2 fuzzy logic with optimization algorithms, powerful hybrid intelligent systems have been built using the advantages that each technique offers. This book is intended to be a reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation. This book can also be used as a reference for graduate courses like the following: soft computing, intelligent pattern recognition, computer vision, applied artificial intelligence, and similar ones. We consider that this book can also be used to get novel ideas for new lines of re-search, or to continue the lines of research proposed by the authors.

Introduction.- Type-2 Fuzzy Logic Systems.- Bio-Inspired Optimization Methods.- Overview of Genetic Algorithms Applied in the Optimization of Type-2 Fuzzy Systems.- Particle Swarm Optimization in the Design of Type-2 Fuzzy Systems.- Ant Colony Optimization Algorithms for the Design of Type-2 Fuzzy Systems.- Other Methods for Optimization of Type-2 Fuzzy Systems.- Simulation Results Illustrating the Optimization of Type-2 Fuzzy Controllers.- Genetic Optimization of Interval Type-2 Fuzzy Sys-tems for Hardware Implementation on FPGAs.- General Overview of the Area and Future Trends.

Reviews current state of the art methods for building intelligent systems using type-2 fuzzy logic and bio-inspired optimization techniques Reference for scientists and engineers interested in applying type-2 fuzzy logic for solving problems in pattern recognition, intelligent control, intelligent manufacturing, robotics and automation Written by leading experts in the field Includes supplementary material: sn.pub/extras