We describe in this book, hybrid intelligent systems using type-2 fuzzy logic and modular neural networks for pattern recognition applications. Hybrid intelligent systems combine several intelligent computing paradigms, including fuzzy logic, neural networks, and bio-inspired optimization algorithms, which can be used to produce powerful pattern recognition systems. The book is organized in three main parts, which contain a group of chapters around a similar subject. The first part consists of chapters with the main theme of theory and design algorithms, which are basically chapters that propose new models and concepts, which can be the basis for achieving intelligent pattern recognition. The second part contains chapters with the main theme of using type-2 fuzzy models and modular neural networks with the aim of designing intelligent systems for complex pattern recognition problems. The third part contains chapters with the theme of evolutionary optimization of type-2 fuzzy systems and modular neural networks in intelligent pattern recognition, which includes the application of genetic algorithms for obtaining optimal type-2 fuzzy integration systems and ideal neural network architectures.

In the part of theory and algorithms there are 4 chapters that describe different contributions that propose new models and concepts, which can be the considered as the basis for achieving intelligent pattern recognition. The first chapter offers an introduction to the areas of type-2 fuzzy logic and modular neural networks for pattern recognition applications. The second chapter describes the basic concepts of type-2 fuzzy logic applied to the problem of edge detection in digital images. The third chapter describes a general methodology for applying type-2 fuzzy logic on improving the recognition ability of modular neural networks. The fourth chapter describes the use of type-2 fuzzy systems for improving the performance of response integration in modular neural networks.