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Optimization of Type 2 Fuzzy Controllers Using the Bee Colony Algorithm
This book focuses on the fields of fuzzy logic, bio-inspired algorithm; especially bee colony optimization algorithm and also considering;
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Differential Evolution Algorithm with Type-2 Fuzzy Logic for Dynamic Parameter Adaptation with Application to Intelligent Control
Generalized Type-2 fuzzy systems. When we use fuzzy systems combined with the differential evolution algorithm, we can notice that the results;
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Advances in Swarm Intelligence
; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; brain storm optimization algorithm; cuckoo searh;
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Advances in Swarm Intelligence
; artificial bee colony algorithms; genetic algorithms; differential evolution; fireworks algorithm; brain storm optimization algorithm; cuckoo searh;
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New Backpropagation Algorithm with Type-2 Fuzzy Weights for Neural Networks
In this book a neural network learning method with type-2 fuzzy weight adjustment is proposed. The mathematical analysis of the proposed;
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Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving;
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Advances in Swarm Intelligence
; theories and models of swarm intelligence; ant colony optimization; particle swarm optimization; artificial bee colony algorithms; genetic;
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Auto Tuning of PID And Fuzzy Controllers Using Genetic Algorithm
In this book, The powerful capabilities of tuning PID and FLC controllers based on Genetic Algorithm (GA) in locating the optimal solutions;
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Design of GA-Fuzzy Controller for Magnetic Levitation Using FPGA
. Genetic Algorithm (GA) is used in this book as optimization method that optimizes the membership, output gain and inputs gain of the fuzzy;
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Advances in Swarm and Computational Intelligence
-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;
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Advances in Swarm and Computational Intelligence
-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;
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Advances in Swarm and Computational Intelligence
-based optimization algorithms and applications; particle swarm opt8imization; ant colony optimization; artificial bee colony algorithms;
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Metaheuristic Optimization
. Major nature-inspired algorithms discussed in this book include the bee colony algorithm, ant colony algorithm, grey wolf optimization algorithm;
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Fuzzy Logic Speed Controllers Using FPGA Technique
. For comparison purpose, two widely used controllers are realized using the same FPGA kit: PID and Fuzzy. Simulations are performed using;
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Optimizing Engineering Problems through Heuristic Techniques
optimization, bee algorithm, firefly optimization, and cuckoo search Offers sample problems and their optimization, using various heuristic;
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Optimizing Engineering Problems through Heuristic Techniques
ant colony optimization, bee algorithm, firefly optimization, and cuckoo search Offers sample problems and their optimization, using;
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Nature-Inspired Metaheuristic Algorithms
in optimization, including genetic algorithms, bee algorithms, particle swarm optimization, simulated annealing, ant colony optimization, harmony search;
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Vision-Based Mobile Robot Control and Path Planning Algorithms in Obstacle Environments Using Type-2 Fuzzy Logic
the first time in conducted experiments. Four different control algorithms, Type-1 fuzzy logic, Type-2 Fuzzy Logic, Decision Tree Control, and;
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Tracking Performance Of Maglev System Using Type-2 Fuzzy Logic Control
can not fully handle the uncertainties present in real-time systems. Type-2 fuzzy sets that are used in type-2 fuzzy systems can handle such;
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Genetic Algorithms, Fuzzy Systems & Website Classification
This book presents and discusses current research in the study of genetic algorithms, fuzzy systems and website classification. Topics;
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Evolutionary Machine Learning Techniques
, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the pros and cons;
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Evolutionary Machine Learning Techniques
, literature reviews, and the training algorithms for machine learning using classical and modern nature-inspired techniques. It also investigates the;
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Neuro-Fuzzy Equalizers for Mobile Cellular Channels
simulation of wireless channel equalizers using the adaptive-network-based fuzzy inference system (ANFIS). The book highlights a study of currently;
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Neuro-Fuzzy Equalizers for Mobile Cellular Channels
simulation of wireless channel equalizers using the adaptive-network-based fuzzy inference system (ANFIS). The book highlights a study of currently;
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Introduction to Nature-Inspired Optimization
optimization. Lindfield/Penny provide concise coverage to all the major algorithms, including genetic algorithms, artificial bee colony algorithms, ant;
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Fuzzy Logic Augmentation of Neural and Optimization Algorithms Theoretical Aspe
concepts and algorithms based on type-2 fuzzy logic for dynamic parameter adaptation in meta-heuristics. The second part discusses network theory;
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