Skip to main content
Springer Nature Link
Log in

The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming

  • Conference paper
  • First Online:
Artificial Intelligence and Soft Computing (ICAISC 2017)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10246))

Included in the following conference series:

  • 2090 Accesses

Abstract

In this paper a new hybrid method to determine parameters of time-variant non-linear models of dynamic objects is proposed. This method first uses the State Transition Algorithm to create many local models and then applies genetic programming in order to join and simplify those models. This allows to obtain simply model which is not computationally demanding and has high accuracy.

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

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. Jordan, A.J.: Linearization of non-linear state equation. Bull. Pol. Acad. Sci. Tech. Sci. 54(1), 63–73 (2006)

    MATH  Google Scholar 

  2. Bartczuk, Ł.: 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. AISC, vol. 429, pp. 125–134. Springer, Cham (2016). doi:10.1007/978-3-319-28555-9_11

    Google Scholar 

  3. Bartczuk, Ł., Przybył, A., Cpałka, K.: A new approach to nonlinear modelling of dynamic systems based on fuzzy rules. Int. J. Appl. Math. Comput. Sci. 26(3), 603–621 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  4. Bello, O., Holzmann, J., Yaqoob, T., Teodoriu, C.: Application of artificial intelligence methods in drilling system design and operations: a review of the state of the art. J. Artif. Intell. Soft Comput. Res. 5(2), 121–139 (2015)

    Article  Google Scholar 

  5. Bertini, J.R., Nicoletti, M.D.C.: Enhancing constructive neural network performance using functionally expanded input data. J. Artif. Intell. Soft Comput. Res. 6(2), 119–131 (2016)

    Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  7. 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)

    Article  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.: Design of Interpretable Fuzzy Systems. Springer (2017)

    Google Scholar 

  10. Cpałka, K., Łapa, K., Przybył, A.: A new approach to design of control systems using genetic programming. Inf. Technol. Control 44(4), 433–442 (2015)

    Google Scholar 

  11. Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno. Fuzzy systems, neural networks. In: Proceedings of the 2005 IEEE International Joint Conference on IJCNN 2005, vol. 3, pp. 1764–1769 (2005)

    Google Scholar 

  12. Cpałka, K., Rebrova, O., Nowicki, R., Rutkowski, L.: On design of flexible neuro-fuzzy systems for nonlinear modelling. Int. J. Gen. Syst. 42(6), 706–720 (2013)

    Article  MATH  Google Scholar 

  13. Freeman, R., Kokotovic, P.V.: State-space and Lyapunov techniques. Springer Science & Business Media, New York (2008)

    MATH  Google Scholar 

  14. Korytkowski, M.: Novel visual information indexing in relational databases. In: Integrated Computer-aided Engineering, pp. 1–10 (2016). doi:10.3233/ICA-160534

  15. Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)

    Article  MathSciNet  Google Scholar 

  16. Koza, J.R.: On the Programming of Computers by Means of Natural Selection, vol. 1. MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  17. Krawiec, K.: Behavioral Program Synthesis with Genetic Programming, vol. 618. Springer, Switzerland (2016)

    Google Scholar 

  18. Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013. LNCS, vol. 7895, pp. 523–534. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38610-7_48

    Chapter  Google Scholar 

  19. Łapa, K., Cpałka, K., Wang, L.: New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 217–232. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_20

    Chapter  Google Scholar 

  20. Łapa, K., Szczypta, J., Venkatesan, R.: Aspects of structure and parameters selection of control systems using selected multi-population algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9120, pp. 247–260. Springer, Cham (2015). doi:10.1007/978-3-319-19369-4_23

    Chapter  Google Scholar 

  21. Nelles, O.: Nonlinear System Identication: From Classical Approaches to Neural Networks and Fuzzy Models. Springer Science & Business Media (2013)

    Google Scholar 

  22. Nonaka, S., Tsujimura, T., Izumi, K.: Gain design of quasi-continuous exponential stabilizing controller for a nonholonomic mobile robot. J. Artif. Intell. Soft Comput. Res. 6(3), 189–201 (2016)

    Article  Google Scholar 

  23. Potter, M.A., Jong, K.A.: A cooperative coevolutionary approach to function optimization. In: Davidor, Y., Schwefel, H.-P., Männer, R. (eds.) PPSN 1994. LNCS, vol. 866, pp. 249–257. Springer, Heidelberg (1994). doi:10.1007/3-540-58484-6_269

    Chapter  Google Scholar 

  24. Prasad, M., Liu, Y.-T., Li, D.-L., Lin, C.-T., Shah, R.R., Kaiwartya, O.M.: A new mechanism for data visualization with Tsk-Type preprocessed collaborative fuzzy rule based system. J. Artif. Intell. Soft Comput. Res. 7(1), 33–46 (2017)

    Article  Google Scholar 

  25. Przybył, A., Er, M.J.: The idea for the integration of neuro-fuzzy hardware emulators with real-time network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 279–294. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_25

    Chapter  Google Scholar 

  26. Przybył, A., Er, M.J.: The method of hardware implementation of fuzzy systems on FPGA. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9692, pp. 284–298. Springer, Cham (2016). doi:10.1007/978-3-319-39378-0_25

    Google Scholar 

  27. Rutkowski, L., Cpałka, K.: Compromise approach to neuro-fuzzy systems. In: Proceedings of the 2nd Euro-International Symposium on Computation Intelligence. Frontiers in Artificial Intelligence and Applications, vol. 76, pp. 85–90 (2002)

    Google Scholar 

  28. Rutkowski, L., Cpałka, K.: A neuro-fuzzy controller with a compromise fuzzy reasoning. Control Cybern. 31(2), 297–308 (2002)

    MATH  Google Scholar 

  29. Rutkowski, L., Przybyl, A., Cpałka, K.: Novel online speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. IEEE Trans. Ind. Electr. 59(2), 1238–1247 (2012)

    Article  Google Scholar 

  30. Rutkowski, L., Przybył, A., Cpałka, K., Er, M.J.: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13232-2_79

    Chapter  Google Scholar 

  31. Starczewski, J., Rutkowski, L.: Connectionist structures of Type 2 fuzzy inference systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) PPAM 2001. LNCS, vol. 2328, pp. 634–642. Springer, Heidelberg (2002). doi:10.1007/3-540-48086-2_70

    Chapter  Google Scholar 

  32. Zalasiński, M.: New algorithm for on-line signature verification using characteristic global features. Adv. Intell. Syst. Comput. 432, 137–146 (2016)

    Google Scholar 

  33. Zalasiński, M., Cpałka, K.: New algorithm for on-line signature verification using characteristic hybrid partitions. Adv. Intell. Syst. Comput. 432, 147–157 (2016)

    Google Scholar 

  34. Zalasiński, M., Cpałka, K., Rakus-Andersson, E.: An idea of the dynamic signature verification based on a hybrid approach. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 232–246. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_21

    Google Scholar 

  35. Zalasiński, M., Cpałka, K., Rutkowski, L.: A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Appl. Soft Comput. 43, 47–56 (2016)

    Article  Google Scholar 

  36. Zhou, X., Yang, C., Gui, W.: Nonlinear system identification and control using state transition algorithm. Appl. Math. Comput. 226, 169–179 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  37. Zhou, X., Gao, D.Y., Yang, C., Gui, W.: Discrete state transition algorithm for unconstrained integer optimization problems. Neurocomputing 173, 864–874 (2016)

    Article  Google Scholar 

  38. Yin, Z., O’Sullivan, C., Brabazon, A.: An analysis of the performance of genetic programming for realised volatility forecasting. J. Artif. Intell. Soft Comput. Res. 6(3), 155–172 (2016)

    Article  Google Scholar 

  39. Zhou, X., Yang, C., Gui, W.: Initial version of state transition algorithm. In: 2011 Second International Conference on Digital Manufacturing and Automation (ICDMA), pp. 644–647. IEEE (2011)

    Google Scholar 

Download references

Acknowledgment

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

    Łukasz Bartczuk & Piotr Dziwiński

  2. Scientific Research Institute for System Analysis of the Russian Academy of Sciences, Moscow, Russia

    Vladimir G. Red’ko

Authors
  1. Łukasz Bartczuk
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Piotr Dziwiński
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vladimir G. Red’ko
    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. Częstochowa University of Technology, Częstochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Częstochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Częstochowa, Poland

    Rafał Scherer

  4. AGH University of Science and Technology, Kraków, Poland

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Bartczuk, Ł., Dziwiński, P., Red’ko, V.G. (2017). The Concept on Nonlinear Modelling of Dynamic Objects Based on State Transition Algorithm and Genetic Programming. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2017. Lecture Notes in Computer Science(), vol 10246. Springer, Cham. https://doi.org/10.1007/978-3-319-59060-8_20

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59060-8_20

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-59059-2

  • Online ISBN: 978-3-319-59060-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics