Skip to main content
SpringerLink
Log in

Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects

  • Conference paper
  • First Online:
Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part I

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 429))

Abstract

In this paper we shown the applying of gene expression programming algorithm to correction modelling of non-linear dynamic objects. The correction modelling is the non-linear modelling method based on equivalent linearization technique that allows to incorporate in modelling process the known linear model of the same or similar object or phenomenon. The usefulness of the proposed method will be shown on a practical example of the continuous stirred tank reactor modelling.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Heinz, S.: Mathematical Modeling. Springer, Berlin (2011)

    Google Scholar 

  2. Łapa, K., Zalasiński, M., Cpałka, K.: A new method for designing and complexity reduction of neuro-fuzzy systems for nonlinear modelling. Lecture Notes in Artificial Intelligence, vol. 7894, pp. 329–344. Springer (2013)

    Google Scholar 

  3. Ismail, S., Pashilkar, A.A., Ayyagari, R., Sundararajan, N.: Neural-sliding mode augmented robust controller for autolanding of fixed wing aircraft. J. Artif. Intell. Soft Comput. Res. 2(4), 317–330 (2012)

    Google Scholar 

  4. Koza JR., 1992, Genetic Programming: on the programming of computers by means of natural selection, Vol. 1. MIT Press

    Google Scholar 

  5. Ferreira, C.: Gene Expression Programming: Mathematical Modeling by an Artificial Intelligence, 2nd edn. Springer, Germany (2006)

    Google Scholar 

  6. Bartczuk, Ł., Przybył A.: New Method for Nonlinear Fuzzy Correction Modelling of Dynamic Objects. Springer LNAI 8467, pp. 169–180 (2014)

    Google Scholar 

  7. Bartczuk, Ł., Przybył, A., Dziwiński, P.: Hybrid State Variables—Fuzzy Logic Modelling of Nonlinear Objects. Springer LNAI 7894, pp. 227–234 (2013)

    Google Scholar 

  8. Cpałka, K., Łapa, K., Przybył, A., Zalasiński, M.: A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. Neurocomputing 135, 203–217 (2014)

    Article  Google Scholar 

  9. Cpałka, K., Łapa, K., Przybył, A., Zalasiński, M., Rutkowski, L.: A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects. Neurocomputing 135, 203–217 (2014)

    Article  Google Scholar 

  10. Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Anal. Ser. A Theor. Methods Appl. 71, 1659–1672 (2009). Elsevier

    Article  Google Scholar 

  11. Kaczorek, T., Dzieliński, A., Dąbrowski, L., Łopatka, R.: The Basis of Control Theory. WNT, Warsaw (2006) (in Polish)

    Google Scholar 

  12. Lobato, F.S., Steffen Jr., V., Silva Neto, A.J.: Solution of singular optimal control problems using the improved differential evolution algorithm. J. Artif. Intell. Soft Comput. Res. 1(3), 195–206 (2011)

    Google Scholar 

  13. Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. Lecture Notes in Artificial Intelligence, vol. 7895, pp. 523–534. Springer (2013)

    Google Scholar 

  14. Prampero, P.S., Attux, R.: Magnetic particle swarm optimization. J. Artif. Intell. Soft Comput. Res. 2(1), 59–72 (2012)

    Google Scholar 

  15. Rutkowski L.: Computational Intelligence: Method and Techniques. Springer, Berlin (2008)

    Google Scholar 

  16. Dziwiński, P., Bartczuk, Ł., Przybył, A., Avedyan, E.D.: A new algorithm for identification of significant operating points using swarm intelligence. Lect. Notes Comput. Sci. 8468, 349–362 (2014). Springer

    Article  Google Scholar 

  17. Folly, K.: Parallel Pbil applied to power system controller design. J. Artif. Intell. Soft Comput. Res. 3(3), 215–223 (2013)

    Article  Google Scholar 

  18. Peteiro-Barral, D., Bardinas, B.G., Perez-Sanchez, B.: Learning from heterogeneously distributed data sets using artificial neural networks and genetic algorithms. J. Artif. Intell. Soft Comput. Res. 2(1), 5–20 (2012)

    Google Scholar 

  19. Theodoridis, D.C., Boutalis, Y.S., Christodoulou, M.A.: Robustifying analysis of the direct adaptive control of unknown multivariable nonlinear systems based on a new neuro-fuzzy method. J. Artif. Intell. Soft Comput. Res. 1(1), 59–79 (2011)

    Google Scholar 

  20. Yang, X.S.: A New metaheuristic bat-inspired algorithm. In: Nature Inspired Cooperative Strategies for Optimization (NISCO 2010), Studies in Computational Intelligence, pp. 65–74. Springer, Berlin (2010)

    Google Scholar 

  21. Caughey, T.K.: Equivalent linearization techniques. J. Acoust. Soc. Am. 35(11), 1706–1711 (1963)

    Article  MathSciNet  Google Scholar 

  22. Ishibuchi H., Doi T., and Nojima Y (2006) Incorporation of scalarizing fitness functions into evolutionary multiobjective optimization algorithms, LNCS, vol. 4193, pp. 493–502. Springer

    Google Scholar 

  23. Vojtesek, J., Dostal, P: From steady-state and dynamic analysis to adaptive control of the CSTR reactor. In: Proceedings of 19th European Conference on Modelling and Simulation ESM, pp. 591–598

    Google Scholar 

  24. Przybył, A., Cpałka, K.: A new method to construct of interpretable models of dynamic systems. Lecture Notes in Artificial Intelligence, vol. 7268, pp. 697–705 Springer, Berlin (2012)

    Google Scholar 

  25. Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Berlin (2003)

    Google Scholar 

  26. Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs. Springer, Berlin (1996)

    Google Scholar 

  27. Kamyar, M.: Takagi-Sugeno fuzzy modeling for process control industrial automation, robotics and artificial intelligence (EEE8005). Sch. Electr. Electron. Comput. Eng. 8 (2008)

    Google Scholar 

  28. McClamroch N.H.: State Models of Dynamic Systems. A Case Study Approach. Springer, Berlin (1980)

    Google Scholar 

  29. Ferreira, C.: Gene expression programming: a new algorithm for solving problems. Complex Syst. 13(2), 87–129 (2001)

    MATH  Google Scholar 

  30. Ferreira, C.: Gene expression programming in problem solving. Soft Computing and Industry, pp. 635–653. Springer, London (2002)

    Google Scholar 

  31. Chaibakhsh, A., Chaibakhsh, N., Abbasi, M., Norouzi, A.: Orthonormal basis function fuzzy systems for biological wastewater treatment processes modeling. Journal of Artificial Intelligence and Soft Computing Research 2(4), 343–356 (2012)

    Google Scholar 

  32. Tran, V.N., Brdys, M.A.: Optimizing control by robustly feasible model predictive control and application to drinking water distribution systems. J. Artif. Intell. Soft Comput. Res 1(1), 43–57 (2011)

    Google Scholar 

Download references

Acknowledgement

The project was financed by the National Science Center on the basis of the decision number DEC-2012/05/B/ST7/02138.

Author information

Authors and Affiliations

  1. Częstochowa University of Technology, Institute of Computational Intelligence, Częstochowa, Poland

    Łukasz Bartczuk

Authors
  1. Łukasz Bartczuk
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Łukasz Bartczuk .

Editor information

Editors and Affiliations

  1. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wroclaw, Poland

    Leszek Borzemski

  2. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wroclaw, Poland

    Adam Grzech

  3. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wrocław, Poland

    Jerzy Świątek

  4. Faculty of Computer Science and Manageme, Wrocław University of Technology, Wrocła, Wrocław, Poland

    Zofia Wilimowska

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this paper

Cite this paper

Bartczuk, Ł. (2016). Gene Expression Programming in Correction Modelling of Nonlinear Dynamic Objects. In: Borzemski, L., Grzech, A., Świątek, J., Wilimowska, Z. (eds) Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part I. Advances in Intelligent Systems and Computing, vol 429. Springer, Cham. https://doi.org/10.1007/978-3-319-28555-9_11

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-28555-9_11

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-28553-5

  • Online ISBN: 978-3-319-28555-9

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation