
New Directions on Hybrid Intelligent Systems Based on Neural Networks, Fuzzy Logic, and Optimization Algorithms
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This book contains a collection of papers focused on hybrid intelligent systems based on soft computing techniques. In this book, new directions on the theoretical developments of fuzzy logic, neural networks and optimization algorithms, as well as their hybrid combinations, are offered. In addition, the abovementioned methods are discussed in application areas such as control and robotics, pattern recognition, medical diagnosis, decision-making, prediction and optimization of complex problems. There are a group of papers with the main theme of type-1, type-2 and type-3 fuzzy logic, which basically consists of papers that propose new concepts and algorithms based on type-1, type-2 and type-3 fuzzy logic and their applications. There is also a set of papers that offer theoretical concepts and applications of meta-heuristics in different areas. Another group of papers outlines diverse applications of hybrid intelligent systems in real problems. There are also some papers that presenttheory and practice of neural networks in different applications. Finally, there are papers that offer theory and practice of optimization and evolutionary algorithms in different application areas
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Content
Towards Interval Type-3 Fuzzy Parameter Adaptation in Metaheuristics.- An Application of Controlled Sets in Medical Diagnosis.- Dynamic Adjustment of an Artificial Fish Swarm Algorithm using Shadowed Type-2 Fuzzy Logic for Enhancing Benchmark Function Optimization.- a-Interval Valued Fuzzy Sets and a-Interval Valued Fuzzy Subgroups.- Design of a Fuzzy PID controller for the balancing problem of a Quadruped Robot
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