Overview
- Novel optimization methods for process system control
- A novel real time control algorithm, that uses Genetic Algorithm (GA) and Ant Colony Optimization (ACO) algorithm for optimizing PID controller parameters
- Artificial neural networks for modelling complex and non-linear systems
Part of the book series: Studies in Computational Intelligence (SCI, volume 449)
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Table of contents (6 chapters)
Keywords
About this book
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.
Authors and Affiliations
Bibliographic Information
Book Title: Optimization of PID Controllers Using Ant Colony and Genetic Algorithms
Authors: Muhammet Ünal, Ayça Ak, Vedat Topuz, Hasan Erdal
Series Title: Studies in Computational Intelligence
DOI: https://doi.org/10.1007/978-3-642-32900-5
Publisher: Springer Berlin, Heidelberg
eBook Packages: Engineering, Engineering (R0)
Copyright Information: Springer-Verlag Berlin Heidelberg 2013
Hardcover ISBN: 978-3-642-32899-2Published: 08 September 2012
Softcover ISBN: 978-3-642-43477-8Published: 15 October 2014
eBook ISBN: 978-3-642-32900-5Published: 13 September 2012
Series ISSN: 1860-949X
Series E-ISSN: 1860-9503
Edition Number: 1
Number of Pages: XX, 88
Topics: Computational Intelligence, Control and Systems Theory, Artificial Intelligence