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

  • Zuzana Kominkova OplatkovaEmail author
  • Roman Senkerik
Part of the Simulation Foundations, Methods and Applications book series (SFMA)


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.


Analytic programming Differential evolution Control law Pseudo neural network 



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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Faculty of Applied InformaticsTomas Bata University in ZlinZlinCzech Republic

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