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Optimization of PID Controllers Using Ant Colony and Genetic Algorithms

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Published by Springer Berlin Heidelberg, Imprint: Springer in Berlin, Heidelberg .
Written in English

Subjects:

  • Control,
  • Engineering,
  • Computational intelligence,
  • Artificial intelligence,
  • Artificial Intelligence (incl. Robotics)

Book details:

Edition Notes

Statementby Muhammet Ünal, Ayça Ak, Vedat Topuz, Hasan Erdal
SeriesStudies in Computational Intelligence -- 449
ContributionsAk, Ayça, Topuz, Vedat, Erdal, Hasan, SpringerLink (Online service)
Classifications
LC ClassificationsQ342
The Physical Object
Format[electronic resource] /
ID Numbers
Open LibraryOL27079034M
ISBN 109783642329005

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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. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms (Studies in Computational Intelligence) [Ünal, Muhammet, Ak, Ayça, Topuz, Vedat, Erdal, Hasan] on *FREE* shipping on qualifying offers. Optimization of PID Controllers Using Ant Colony and Genetic Algorithms (Studies in Computational Intelligence)Format: Paperback. This book covers the theory behind artificial neural networks, genetic algorithms and the ant colony optimization algorithm, and presents a novel real time control algorithm using genetic and . Get this from a library! Optimization of PID controllers using ant colony and genetic algorithms. [Muhammet Ünal;] -- 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.

This book covers the theory behind artificial neural networks, genetic algorithms and the ant colony optimization algorithm, and presents a novel real time control algorithm using genetic and ant colony optimization algorithms for optimizing PID controllers. 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. springer, 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. Ünal M., Ak A., Topuz V., Erdal H. () Ant Colony Optimization (ACO). In: Optimization of PID Controllers Using Ant Colony and Genetic Algorithms. Studies in Computational Intelligence, vol Cited by: 7.

Search Tips. Phrase Searching You can use double quotes to search for a series of words in a particular order. For example, "World war II" (with quotes) will give more precise results than World war II (without quotes). Wildcard Searching If you want to search for multiple variations of a word, you can substitute a special symbol (called a "wildcard") for one or more letters. In the ant colony optimization algorithms, an artificial ant is a simple computational agent that searches for good solutions to a given optimization problem. To apply an ant colony algorithm, the optimization problem needs to be converted into the problem of finding the shortest path on a weighted graph. In the first step of each iteration. Abstract. This paper treats a tuning of PID controllers method using multiobjective ant colony optimization. The design objective was to apply the ant colony algorithm in the aim of tuning the optimum solution of the PID controllers (,, and) by minimizing the multiobjective potential of using multiobjective ant algorithms is to identify the Pareto optimal by:   This is a graphical TSP solver. It uses several methods for solving: Ant System, MaxMIn AntSystem, AntColonySistem, Genetic Algoritm and Genetic AntSystem. G.