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1. Auflage 2009
422 Seiten, A5, broschiert,
ISBN 978-3-85499-569-2
Art. Nr. 20134591
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EUR 22,50 inkl. USt.
zzgl. Versandkosten |
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System identification denotes the data driven generation of mathematical models for systems; the result of a system identification algorithm consists in a mathematical description of the behavior of the analyzed system. Evolutionary computation is a subfield of computational intelligence that uses concepts inspired by natural evolution; one of the most famous evolutionary techniques is the genetic algorithm, a global optimization technique using aspects inspired by evolutionary biology such as selection, recombination, mutation and inheritance.
This thesis concentrates on evolutionary system identification techniques based on genetic programming (GP), an extension of the genetic algorithm: Mathematical expressions are produced by an evolutionary process that uses the given measurement data.
The first part of this thesis describes theoretical concepts used in this work as well as our GP implementation for the HeuristicLab framework. Concepts for monitoring population dynamics during the execution of the GP process are also described; we here concentrate on genetic diversity and genetic propagation. The application of advanced selection principles and optimization stages is also explained as well as on-line and sliding window GP variants.
The second part of this thesis summarizes the results of system identification test series; the data sets used here include dynamic measurement data of mechatronical systems as well as classification benchmark problems. The results of these tests demonstrate the ability of this method to pro-duce models of high quality for different kinds of machine learning problems, and also give insights into population dynamic processes that occur during the execution of a GP process.
Dipl.-Ing. Dr. Stephan Winkler,
Studium der Informatik und Doktoratsstudium an der JKU in Linz. Bis 2006 wissenschaftlicher Mitarbeiter am LCM und am Institut für Design und Regelung mechatronischer Systeme, danach Anstellung im Rahmen des FWF Translational Research Projekts L284 "GP-Based Techniques fort he Design Virtual Sensors" an der FH OÖ, Campus Hagenberg. Ab Februar 2009 Antritt einer Professur für Bioinformatik an der FH OÖ.
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