RBF Neural Network for Identification and Control Using PAC

  • Ladislav Körösi
  • Vojtech Németh
  • Jana Paulusová
  • Štefan Kozák
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 239)

Abstract

In this paper the implementation of RBF online learning algorithm on the Schneider Electric Quantum programmable automation controllers is proposed. Online recursive mean square algorithm with different modifications is proposed for neural network parameter identification. All matrix operations, functions, algorithms and neural network general structure are programmed in the Structured Text programming language in Unity Pro XL software. The proposed method and software implementation is verified on virtual hydraulic system with parameter identification and level control.

Keywords

Neural network RBF online learning programmable automation controllers 

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References

  1. 1.
    Oravec, M., Polec, J., Marchevský, S.: Neural networks for digital signal processing. FABER, Bratislava (1998) (in Czech)Google Scholar
  2. 2.
    Haykin, S.: Neural networks – A Comprehensive Foundation. Macmillan College Publishing Company, New York (1994)Google Scholar
  3. 3.
    Körösi, L., Kozák, Š.: Optimal Self Tuning Neural Network Controller Design. In: 16th IFAC World Congress, Praha, Czech repubic (2005)Google Scholar
  4. 4.
    Jadlovská, A.: Modeling and control of dynamic processes using neural networks, Košice (2003) ISBN 80-88941 -22 -9 (in Slovak)Google Scholar
  5. 5.
    Körösi, L.: Contribution to the problem of optimizing the structures of artificial neural networks. Institute of Control and Industrial Informatics, FEI STU, Bratislava, 126 p. (2010) (in Slovak)Google Scholar
  6. 6.
    Körösi, L.: Neural Network Modeling and Control Using Programmable Logic Controller. Posterus, 4(12) (2011), ISSN 1338-0087, http://www.posterus.sk/?p=12304
  7. 7.
    Körösi, L., Kelemen, J.: Implementation of Orthogonal Neural Network Learning on PLC. In: Kybernetika a Informatika, 20th International Conference SSKI and FEI STU, Skalka pri Kremnici, Bratislava, Januay 31-February 4, pp. 87–88 (2012)Google Scholar
  8. 8.
    Paulusová, J., Dúbravská, M.: Neuro Fuzzy Predictive Control. International Review of Automatic Control 5(5), s. 667–s. 672 (2012) ISSN 1974-6059 - ISSN 1974-6067Google Scholar
  9. 9.
    Jadlovská, A., Jajčišin, Š.: Using Neural Networks for Physical Systems Behavior Prediction. In: AEI 2012 Conference: Applied Electrical Engineering and Informatics: Proc. FEI TU, Košice (2012) ISBN 978-80-553-1030-5Google Scholar
  10. 10.
    Körösi, L., Mrafko, L., Mrosko, M.: PLC and their Programming - 2. PLC, PAC, DCS – Who Whom? Posterus 4(10) (2011), ISSN 1338-0087, http://www.posterus.sk/?p=11925

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Ladislav Körösi
    • 1
  • Vojtech Németh
    • 1
  • Jana Paulusová
    • 1
  • Štefan Kozák
    • 1
  1. 1.Institute of Control and Industrial Informatics, Faculty of Electrical Engineering, and Information TechnologySlovak University of TechnologyBratislavaSlovak Republic

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