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Seminal Contributions to Modelling and Simulation pp 91–113Cite as

Control Law and Pseudo Neural Networks Synthesized by Evolutionary Symbolic Regression Technique

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Part of the book series: Simulation Foundations, Methods and Applications ((SFMA))

Abstract

This research deals with synthesis of final complex expressions by means of an evolutionary symbolic regression technique—analytic programming (AP)—for novel approach to classification and system control. In the first case, classification technique—pseudo neural network is synthesized, i.e. relation between inputs and outputs created. The inspiration came from classical artificial neural networks where such a relation between inputs and outputs is based on the mathematical transfer functions and optimized numerical weights. AP will synthesize a whole expression at once. The latter case, the AP will create chaotic controller that secures the stabilization of stable state and high periodic orbit—oscillations between several values of discrete chaotic system. Both cases will produce a mathematical relation with several inputs, the latter case uses several historical values from the time series. For experimentation, Differential Evolution (DE) for the main procedure and also for meta-evolution version of analytic programming (AP) was used.

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Acknowledgement

This work was supported by the Ministry of Education, Youth and Sports of the Czech Republic within the National Sustainability Programme project No. LO1303 (MSMT-7778/2014) and also by the European Regional Development Fund under the project CEBIA-Tech No. CZ.1.05/2.1.00/03.0089, further it was supported by Grant Agency of the Czech Republic—GACR 588P103/15/06700S.

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Authors and Affiliations

  1. Faculty of Applied Informatics, Tomas Bata University in Zlin, Nam T.G. Masaryka 5555, 760 01, Zlin, Czech Republic

    Zuzana Kominkova Oplatkova & Roman Senkerik

Authors
  1. Zuzana Kominkova Oplatkova
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  2. Roman Senkerik
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Correspondence to Zuzana Kominkova Oplatkova .

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Editors and Affiliations

  1. Faculty of Computing, Engineering and Science, University of South Wales, Pontypridd, United Kingdom

    Khalid Al-Begain

  2. ECMS, Nottingham, United Kingdom

    Andrzej Bargiela

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Oplatkova, Z.K., Senkerik, R. (2016). Control Law and Pseudo Neural Networks Synthesized by Evolutionary Symbolic Regression Technique. In: Al-Begain, K., Bargiela, A. (eds) Seminal Contributions to Modelling and Simulation. Simulation Foundations, Methods and Applications. Springer, Cham. https://doi.org/10.1007/978-3-319-33786-9_9

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  • DOI: https://doi.org/10.1007/978-3-319-33786-9_9

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  • Publisher Name: Springer, Cham

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  • Online ISBN: 978-3-319-33786-9

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