Titlebook: Computational Intelligence and Optimization Methods for Control Engineering; Maude Josée Blondin,Panos M. Pardalos,Javier Sanch Book 2019

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Fuzzy and Neuro-fuzzy Control for Smart Structures,heir applicability, their efficiency, and their robustness. In other words, the controllers can be designed based on intuition and basic engineering principles, and then they can be subjected to optimization, e.g., to training/learning using artificial neural networks, in order to achieve certain properties.

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https://doi.org/10.1007/978-3-662-59442-1g are the most known. A possible acceleration of the convergence can be obtained by using tunneling algorithms. Concerning the global methods, the Genetic or Evolution Algorithms (GA), Ant Colony Optimization (ACO), and the Particle Swarm Optimization (PSO) are the most known.

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https://doi.org/10.1007/3-540-29983-1butes a new dataset of detailed power consumption data that was captured in a residence that was specially set up. The results show that by analyzing overlapping power patterns that electrical appliances generate, and a resident-level energy meter of adequate granularity, appliance identification becomes possible.

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,Ma?regeln der Besserung und Sicherung, past decades due to technological advances and technology affordability. Nowadays, almost all engineering activities exploit automatic control. Therefore, this chapter aims to provide the core knowledge concerning some of the most important features in control design and its methods. It covers basi

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https://doi.org/10.1007/978-3-662-59442-1eers prefer to use metaheuristics which are able to produce good solutions in a reasonable computation time. The metaheuristic approaches can be separated into two classes: the local search techniques and the global ones. Among the local search techniques, the taboo search and the simulated annealin