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A New Method of the Intelligent Modeling of the Nonlinear Dynamic Objects with Fuzzy Detection of the Operating Points

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Artificial Intelligence and Soft Computing (ICAISC 2016)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9693))

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Abstract

The paper presents a new method of the intelligent modeling of the nonlinear dynamic objects with online detection of significant operating points from non-invasive measurements of the nonlinear dynamic object. The PSO-GA algorithm is used to identify the unknown values of the system matrix describing the nonlinear dynamic object in the detected operating points. The Takagi-Sugeno fuzzy system determines the values of the system matrix in the detected operating points. The new method was tested on the nonlinear electrical circuit with the three operating points. The obtained results prove efficiency of the new method of the intelligent modeling of the nonlinear dynamic objects with fuzzy detection of the operating points.

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References

  1. Aghdam, M.H., Heidari, S.: Feature selection using particle swarm optimization in text categorization. J. Artif. Intell. Soft Comput. Res. 5(4), 231–238 (2015)

    Article  Google Scholar 

  2. Bartczuk, Ł.: Gene expression programming in correction modelling of nonlinear dynamic objects. Adv. Intell. Syst. Comput. 429, 125–134 (2016)

    Article  Google Scholar 

  3. Bartczuk, Ł., Rutkowska, D.: Medical diagnosis with Type-2 fuzzy decision trees. In: Kącki, E., Rudnicki, M., Stempczyńska, J. (eds.) Computers in Medical Activity. AISC, vol. 65, pp. 11–21. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  4. Bartczuk, Ł., Rutkowska, D.: Type-2 fuzzy decision trees. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 197–206. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  5. Bartczuk, Ł., Przybył, A., Koprinkova-Hristova, P.: New method for nonlinear fuzzy correction modelling of dynamic objects. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 169–180. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  6. Bas, E.: The training of multiplicative neuron model based artificial neural networks with differential evolution algorithm for forecasting. J. Artif. Intell. Soft Comput. Res. 6(1), 5–11 (2016)

    Article  Google Scholar 

  7. Chen, M., Ludwig, S.A.: Particle swarm optimization based fuzzy clustering approach to identify optimal number of clusters. J. Artif. Intell. Soft Comput. Res. 4(1), 43–56 (2014)

    Article  Google Scholar 

  8. Chiu, S.: Fuzzy model identification based on cluster estimation. J. Intell. Fuzzy Syst. 2(3), 267–278 (1994)

    Google Scholar 

  9. Cpalka, K.: A method for designing flexible Neuro-fuzzy systems. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 212–219. Springer, Heidelberg (2006)

    Chapter  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. 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). Elsevier

    Article  Google Scholar 

  12. 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 

  13. 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 

  14. 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 

  15. Cpałka, K., Rutkowski, L.: Flexible takagi sugeno neuro-fuzzy structures for nonlinear approximation. WSEAS Trans. Syst. 4(9), 1450–1458 (2005)

    Google Scholar 

  16. Cpałka, K., Rutkowski, L.: A new method for designing and reduction of Neuro-fuzzy systems. In: Proceedings of the 2006 IEEE International Conference on Fuzzy Systems (IEEE World Congress on Computational Intelligence, WCCI 2006), Vancouver, pp. 8510–8516 (2006)

    Google Scholar 

  17. 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 

  18. Cpałka, K., Zalasiń, S.M.: On-line signature verification using vertical signature partitioning. Expert Syst. Appl. 41(9), 4170–4180 (2014)

    Article  Google Scholar 

  19. Cpałka, K., Zalasiński, M., Rutkowski, L.: A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Appl. Soft. Comput. 43, 47–56 (2016). http://dx.doi.org/10.1016/j.asoc.2016.02.017

    Article  Google Scholar 

  20. Cpałka, K., Zalasiński, M., Rutkowski, L.: New method for the on-line signature verification based on horizontal partitioning. Pattern Recogn. 47, 2652–2661 (2014)

    Article  Google Scholar 

  21. Duch, W., Korbicz, J., Rutkowski, L., Tadeusiewicz, R. (eds.): Biocybernetics and Biomedical Engineering 2000. Neural Networks, vol. 6, Akademicka Oficyna Wydawnicza, EXIT, Warsaw, (in Polish) (2000)

    Google Scholar 

  22. Dziwiñski, P., Rutkowska, D.: Algorithm for generating fuzzy rules for WWW document classification. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Żurada, J.M. (eds.) ICAISC 2006. LNCS (LNAI), vol. 4029, pp. 1111–1119. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  23. Dziwiński, P., Bartczuk, Ł., Przybył, A., Avedyan, E.D.: A new algorithm for identification of significant operating points using swarm intelligence. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 349–362. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  24. Dziwiński, P., Avedyan, E.D.: A new approach to nonlinear modeling based on significant operating points detection. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 364–378. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  25. El-Samak, A.F., Ashour, W.: Optimization of traveling salesman problem using affinity propagation clustering and genetic algorithm. J. Artif. Intell. Soft Comput. Res. 5(4), 239–245 (2015)

    Article  Google Scholar 

  26. Eftekhari, M., Zeinalkhani, M.: Extracting interpretable fuzzy models for nonlinear systems using gradient-based continuous ant colony optimization. Fuzzy Inf. Eng. 5, 255–277 (2013). Springer

    Article  MathSciNet  Google Scholar 

  27. Eftekhari, M., Deai, B., Katebi, S.D.: Gradient-based ant colony optimization for continuous spaces. Esteghlal J. Eng. 25, 33–45 (2006)

    Google Scholar 

  28. Gałkowski, T., Rutkowski, L.: Nonparametric recovery of multivariate functions with applications to system identification. Proc. IEEE 73(5), 942–943 (1985)

    Article  Google Scholar 

  29. Juang, C.-F.: A hybrid of genetic algorithm and particle swarm optimization for recurrent network design. IEEE Trans. Syst. Man Cybern. Part B Cybern. 34(2), 997–1006 (2004)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  31. Korytkowski, M., Rutkowski, L., Scherer, R.: From ensemble of fuzzy classifiers to single fuzzy rule base classifier. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 265–272. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  32. 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, Part I. LNCS, vol. 8467, pp. 217–232. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  33. 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, Part II. LNCS, vol. 7895, pp. 523–534. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  34. Łapa, K., Zalasiński, M., Cpałka, K.: A new method for designing and complexity reduction of Neuro-fuzzy systems for nonlinear modelling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 329–344. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  35. Li, X., Er, M.J., Lim, B.S.: Fuzzy regression modeling for tool performance prediction and degradation detection. Int. J. Neural Syst. 20, 405–419 (2010)

    Article  Google Scholar 

  36. Ludwig, S.A.: Repulsive self-adaptive acceleration particle swarm optimization approach. J. Artif. Intell. Soft Comput. Res. 4(3), 189–204 (2014)

    Article  Google Scholar 

  37. Arain, M.A., Hultmann Ayala, H.V., Ansari, M.A.: Nonlinear system identification using neural network. In: Chowdhry, B.S., Shaikh, F.K., Hussain, D.M.A., Uqaili, M.A. (eds.) IMTIC 2012. CCIS, vol. 281, pp. 122–131. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  38. Przybył, A., Cpałka, K.: A new method to construct of interpretable models of dynamic systems. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 697–705. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  39. Rutkowski, L.: Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Trans. Neural Networks 15(4), 811–827 (2004)

    Article  MathSciNet  Google Scholar 

  40. Rutkowski, L.: A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Trans. Circ. Syst. 33(8), 812–818 (1986)

    Article  MATH  Google Scholar 

  41. Rutkowski, L.: Application of multiple Fourier-series to identification of multivariable non-stationary systems. Int. J. Syst. Sci. 20(10), 1993–2002 (1989)

    Article  MathSciNet  MATH  Google Scholar 

  42. Rutkowski, L., Cpałka, K.: Compromise approach to neuro-fuzzy systems. In: Sincak, P., Vascak, J., Kvasnicka, V., Pospichal, J. (eds.) Intelligent Technologies - Theory and Applications, vol. 76, pp. 85–90. IOS Press, The Netherlands (2002)

    Google Scholar 

  43. Rutkowski, L., Cpałka, K.: Flexible structures of neuro-fuzzy systems. Quo Vadis Comput. Intell. Stud. Fuzziness Soft. Comput. 54, 479–484 (2000). Springer

    Google Scholar 

  44. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: A new method for data stream mining based on the misclassification error. IEEE Trans. Neural Netw. Learn. Syst. 26(5), 1048–1059 (2015)

    Article  MathSciNet  Google Scholar 

  45. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees for mining data streams based on the gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)

    Article  Google Scholar 

  46. Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014)

    Article  Google Scholar 

  47. Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Eng. 25(6), 1272–1279 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  49. 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, Part II. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  50. Starczewski, J.T., Bartczuk, Ł., Dziwiński, P., Marvuglia, A.: Learning methods for type-2 FLS based on FCM. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part I. LNCS, vol. 6113, pp. 224–231. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  51. Starczewski, J., Rutkowski, L.: Interval type 2 neuro-fuzzy systems based on interval consequents. In: Rutkowski, L., Kacprzyk, J. (eds.) Neural Networks and Soft Computing, pp. 570–577. Physica-Verlag, A Springer-Verlag Company, Heidelberg (2003)

    Chapter  Google Scholar 

  52. Starczewski, J.T., 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)

    Chapter  Google Scholar 

  53. Tambouratzis, T., Souliou, D., Chalikias, M., Gregoriades, A.: Maximising accuracy and efficiency of traffic accident prediction combining information mining with computational intelligence approaches and decision trees. J. Artif. Intell. Soft Comput. Res. 4(1), 31–42 (2014)

    Article  Google Scholar 

  54. Xinghua, L., Jiang, M., Jike, G.: A method research on nonlinear system identification based on neural network. Information Engineering and Applications. LNEE, vol. 154, pp. 234–240. Springer, London (2012)

    Google Scholar 

  55. Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012, Part II. LNCS, vol. 7268, pp. 362–367. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  56. Zalasiński, M., Cpałka, K., Er, M.J.: A new method for the dynamic signature verification based on the stable partitions of the signature. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 161–174. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  57. Zalasiński, M., Cpałka, K., Er, M.J.: New method for dynamic signature verification using hybrid partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 216–230. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  58. Zalasiński, M., Cpałka, K.: Novel algorithm for the on-line signature verification using selected discretization points groups. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 493–502. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  59. Zalasiński, M., Cpałka, K., Hayashi, Y.: New fast algorithm for the dynamic signature verification using global features values. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 175–188. Springer, Heidelberg (2015)

    Chapter  Google Scholar 

  60. Zalasiński, M., Cpałka, K., Hayashi, Y.: New method for dynamic signature verification based on global features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part II. LNCS, vol. 8468, pp. 231–245. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  61. Zalasiński, M., Łapa, K., Cpałka, K.: New algorithm for evolutionary selection of the dynamic signature global features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 113–121. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

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Acknowledgments

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

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Dziwiński, P., Avedyan, E.D. (2016). A New Method of the Intelligent Modeling of the Nonlinear Dynamic Objects with Fuzzy Detection of the Operating Points. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L., Zurada, J. (eds) Artificial Intelligence and Soft Computing. ICAISC 2016. Lecture Notes in Computer Science(), vol 9693. Springer, Cham. https://doi.org/10.1007/978-3-319-39384-1_25

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