This book deals with the theory, design principles, and application of hybrid intelligent systems using type-2 fuzzy sets in combination with other paradigms of Soft Computing technology such as Neuro-Computing and Evolutionary Computing. It provides a self-contained exposition of the foundation of type-2 fuzzy neural networks and presents a vast compendium of its applications to control, forecasting, decision making, system identification and other real problems. Type-2 Fuzzy Neural Networks and Their Applications is helpful for teachers and students of universities and colleges, for scientists and practitioners from various fields such as control, decision analysis, pattern recognition and similar fields.
Explores the theory, design principles and applications of hybrid intelligent systems that use type-2 fuzzy sets in combination with other paradigms of Soft Computing technology, such as Neuro-Computing and Evolutionary Computing Provides a comprehensive examination of type-2 fuzzy neural networks and its applications in control, forecasting, decision making and system identification Includes source code in C# for implementing one of the outlined examples of a fuzzy type-2 neural network Includes supplementary material: sn.pub/extras
Rafik Aziz Aliev
DEO based network training differential evolution evolutionary computing fuzzy neural network fuzzy set fuzzy wavelet neural network neural network recurrent neural network type-2 fuzzy neural network type-2 fuzzy set