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Optimization of PID Controllers Using Ant Colony and Genetic Algorithms [electronic resource] / by Muhammet �Unal, Ay�ca Ak, Vedat Topuz, Hasan Erdal.

By: �Unal, Muhammet [author.].
Contributor(s): Ak, Ay�ca [author.] | Topuz, Vedat [author.] | Erdal, Hasan [author.] | SpringerLink (Online service).
Material type: materialTypeLabelBookSeries: Studies in Computational Intelligence: 449Publisher: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2013Description: XX, 88 p. online resource.Content type: text Media type: computer Carrier type: online resourceISBN: 9783642329005.Subject(s): Engineering | Artificial intelligence | Computational intelligence | Control engineering | Engineering | Computational Intelligence | Control | Artificial Intelligence (incl. Robotics)Additional physical formats: Printed edition:: No titleDDC classification: 006.3 Online resources: Click here to access online
Contents:
Artificial Neural Networks -- Genetic Algorithm -- Ant Colony Optimization (ACO) -- An Application for Process System Control.
In: Springer eBooksSummary: Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to  process system control.
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Artificial Neural Networks -- Genetic Algorithm -- Ant Colony Optimization (ACO) -- An Application for Process System Control.

Artificial neural networks, genetic algorithms and the ant colony optimization algorithm have become a highly effective tool for solving hard optimization problems. As their popularity has increased, applications of these algorithms have grown in more than equal measure. While many of the books available on these subjects only provide a cursory discussion of theory, the present book gives special emphasis to the theoretical background that is behind these algorithms and their applications. Moreover, this book introduces a novel real time control algorithm, that uses genetic algorithm and ant colony optimization algorithms for optimizing PID controller parameters. In general, the present book represents a solid survey on artificial neural networks, genetic algorithms and the ant colony optimization algorithm and introduces novel practical elements related to the application of these methods to  process system control.

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