Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine

This book describes the latest advances in fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems. The book is divided into five main parts. The first part proposes new concepts and algorithms based on type-1 and type-2 fuzzy logic and their applications; the second explores new concepts and algorithms in neural networks and fuzzy logic applied to recognition. The third part examines the theory and practice of meta-heuristics in various areas of application, while the fourth highlights diverse applications of fuzzy logic, neural networks and hybrid intelligent systems in medical contexts. Finally, the fifth part focuses on applications of fuzzy logic, neural networks and meta-heuristics to robotics problems.

Presents the latest research on Intelligent Systems applied to Control, Pattern Recognition and Medicine Describes recent advances in fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, and their applications in areas such as: intelligent control and robotics, pattern recognition, medical diagnosis, time series prediction, and optimization of complex problems Written by respected experts in the field

Inhalt
Part I: Type-1 and Type-2 Fuzzy Logic.- Intuitionistic and Neutrosophic Fuzzy Logic: Basic Concepts and Applications.- Study of the Relevance of Polynomial Order in Takagi-Sugeno Fuzzy Inference Systems Applied in Diagnosis Prolems.- Adaptation of Parameters with Binary Cat Swarm Optimization Algorithm of Controller for a Mobile Autonomous Robot.- Comparison of Fuzzy Controller Optimization with Dynamic Parameter Adjustment based on of Type-1 and Type-2 Fuzzy Logic.- Part II: Pattern Recognition.- Particle Swarm Algorithm for the Optimization of Modular Neural Networks in Pattern Recognition.- Optimal recognition model based on convolutional neural networks and fuzzy gravitational search algorithm method.