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A New Method for Generating of Fuzzy Rules for the Nonlinear Modelling Based on Semantic Genetic Programming

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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

In this paper we propose a new approach for nonlinear modelling. It uses capabilities of the Takagi-Sugeno neuro-fuzzy systems and population based algorithms. The aim of our method is to ensure that created model achieves appropriate accuracy and is as compact as possible. In order to obtain this aim we incorporate semantic information about created fuzzy rules into process of evolution. Our method was tested with the use of well-known benchmarks from the literature.

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References

  1. Bartczuk, Ł., Przybył, A., Dziwiński, P.: Hybrid state variables - Fuzzy logic modelling of nonlinear objects. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part I. LNCS, vol. 7894, pp. 227–234. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  2. 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 

  3. 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 

  4. Beadle, L., Johnson, C.: Semantically driven crossover in genetic programming. In: 2008 IEEE Congress on Evolutionary Computation (CEC), pp. 111–116 (2008)

    Google Scholar 

  5. Beadle, L., Johnson, C.: Semantically driven mutation in genetic programming. In: 2009 IEEE Congress on Evolutionary Computation (CEC), pp. 1336–1342 (2009)

    Google Scholar 

  6. Brooks, T.F., Pope, D.S., Marcolini, A.M.: Airfoil self-noise and prediction, Technical report, NASA RP-1218 (1989)

    Google Scholar 

  7. Chaibakhsh, A., Chaibakhsh, N., Abbasi, M., Norouzi, A.: Orthonormal basis function fuzzy systems for biological wastewater treatment processes modeling. J. Artif. Intell. Soft Comput. Res. 2(4), 343–356 (2012)

    Google Scholar 

  8. Chang, W.-J., Chang, W., Liu, H.-H.: Model-based fuzzy modeling and control for autonomous underwater vehicles in the horizontal plane. J. Mar. Sci. Technol. 11(3), 155–163 (2003)

    MathSciNet  Google Scholar 

  9. Chen, Q., Abercrombie, R.K., Sheldon, F.T.: Risk assessment for industrial control systems quantifying availability using mean failure cost (MFC). J. Artif. Intell. Soft Comput. Res. 5(3), 205–220 (2015)

    Article  Google Scholar 

  10. 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 

  11. 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 

  12. 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 

  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. In: Proceedings of the 2005 IEEE International Joint Conference on Neural Networks, 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, BC, Canada, pp. 8510–8516 (2006)

    Google Scholar 

  17. Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of the International Joint Conference on Neural Networks 2005, Montreal, pp. 1764–1769 (2005)

    Google Scholar 

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

    Article  Google Scholar 

  20. 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)

    Article  Google Scholar 

  21. Delgado, M.R., Zuben, F.V., Gomide, F.: Coevolutionary genetic fuzzy systems: a hierarchical collaborative approach. Fuzzy Sets Syst. 141, 89–106 (2004). Elsevier

    Article  MathSciNet  MATH  Google Scholar 

  22. Dziwiński, P., Bartczuk, Ł., Starczewski, J.T.: Fully controllable ant colony system for text data clustering. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) EC 2012 and SIDE 2012. LNCS, vol. 7269, pp. 199–205. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  23. 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 

  24. Dziwiński, P., Rutkowska, D.: Ant focused crawling algorithm. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS (LNAI), vol. 5097, pp. 1018–1028. Springer, Heidelberg (2008)

    Chapter  Google Scholar 

  25. Dziwiński, P., Starczewski, J.T., Bartczuk, Ł.: New linguistic hedges in construction of interval type-2 FLS. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 445–450. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  26. 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 

  27. 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 

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

    MATH  Google Scholar 

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

    Article  Google Scholar 

  30. Galvan-Lopez, E., Cody-Kenny, B., Trujillo, L., Kattan, A.: Using semantics in the selection mechanism in genetic programming: a simple method for promoting semantic diversity. In: 2013 IEEE Congress on Evolutionary Computation (CEC), pp. 2972–2979 (2013)

    Google Scholar 

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

    Article  Google Scholar 

  32. Hoffman, F., Nelles, O.: Genetic programming for model selection of TSK-fuzzy systems. Inf. Sci. 136, 7–28 (2001). Elsevier

    Article  MATH  Google Scholar 

  33. 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 

  34. Jimenez, F., Yoshikawa, T., Furuhashi, T., Kanoh, M.: An emotional expression model for educational-support robots. J. Artif. Intell. Soft Comput. Res. 5(1), 51–57 (2015)

    Article  Google Scholar 

  35. Johansen, T.A., Shorten, R., Murray-Smith, R.: On the interpretation and identification of dynamic Takagi-Sugeno fuzzy models. IEEE Trans. Fuzzy Syst. 8(3), 297–313 (2000)

    Article  Google Scholar 

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

    Google Scholar 

  37. Kamyar, M.: Takagi-Sugeno Fuzzy Modeling for Process Control Industrial Automation, Robotics and Artificial Intelligence (EEE8005), School of Electrical, Electronic and Computer Engineering, vol. 8 (2008)

    Google Scholar 

  38. Kibler, D., Aha, D.: Instance-based prediction of real-valued attributes. In: Proceedings of the CSCSI (Canadian AI) Conference (1988)

    Google Scholar 

  39. Koprinkova-Hristova, P.: Backpropagation through time training of a neuro-fuzzy controller. Int. J. Neural Syst. 20(5), 421–428 (2010)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  41. Koshiyama, A.S., Vellasco, M., Tanscheit, R.: Gpfis-control: a genetic fuzzy system for control tasks. J. Artif. Intell. Soft Comput. Res. 4(3), 167–179 (2014)

    Article  Google Scholar 

  42. Koza, J.R.: Genetic programming - On the Programming of Computers by Means of Natural Selection. The MIT Press, Cambridge (1992)

    MATH  Google Scholar 

  43. Krawiec, K.: Genetic programming: where meaning emerges from program code. Genet. Program. Evolvable Mach. 15(1), 75–77 (2014). Springer

    Article  Google Scholar 

  44. Lin, C.H., Dong, F.Y., Hirota, K.: Common driving notification protocol based on classified driving behavior for cooperation intelligent autonomous vehicle using vehicular ad-hoc network technology. J. Artif. Intell. Soft Comput. Res. 5(1), 5–21 (2015)

    Article  Google Scholar 

  45. 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 

  46. 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 

  47. Laskowski, Ł.: A novel hybrid-maximum neural network in stereo-matching process. Neural Comput. Appl. 23(7–8), 2435–2450 (2013). Springer

    Article  Google Scholar 

  48. Laskowski, Ł., Jelonkiewicz, J.: Self-correcting neural network for stereo-matching problem solving. Fundamenta Informaticae 138(4), 457–482 (2015). IOS Press

    Article  MathSciNet  MATH  Google Scholar 

  49. Miyajima, H., Shigei, N., Miyajima, H.: Performance comparison of hybrid electromagnetism-like mechanism algorithms with descent method. J. Artif. Intell. Soft Comput. Res. 5(4), 271–282 (2015)

    Article  MATH  Google Scholar 

  50. 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 

  51. Quinlan, R.: Combining instance-based and model-based learning. In: Proceedings on the Tenth International Conference of Machine Learning, pp. 236–243 (1993)

    Google Scholar 

  52. Rigatos, G.G., Siano, P.: Flatness-based adaptive fuzzy control of spark-ignited engines. J. Artif. Intell. Soft Comput. Res. 4(4), 231–242 (2014)

    Article  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  54. Rutkowski, L.: Computational Intelligence: Methods and Techniques. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  55. Rutkowski, L., Cpałka, K.: Flexible structures of neuro-fuzzy systems. Quo Vadis Computational Intelligence, Studies in Fuzziness and Soft Computing, Springer 54, 479–484 (2000)

    Google Scholar 

  56. Rutkowski, L., Cpałka, K.: A general approach to neuro-fuzzy systems. In: The 10th IEEE International Conference on Fuzzy Systems, 2001, Melbourne, pp. 1428–1431 (2001)

    Google Scholar 

  57. 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. IOS Press, vol. 76, pp. 85–90 (2002)

    Google Scholar 

  58. 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, Amsterdam (2002)

    Google Scholar 

  59. Rutkowski, L., Cpałka, K.: Neuro-fuzzy systems derived from quasi-triangular norms. In: Proceedings of the IEEE International Conference on Fuzzy Systems, Budapest, 26–29 July 2014, vol. 2, pp. 1031–1036 (2004)

    Google Scholar 

  60. 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 

  61. 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 

  62. 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 

  63. 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 

  64. Rutkowski, L., Przybył, A., Cpałka, K.: Novel on-line speed profile generation for industrial machine tool based on flexible neuro-fuzzy approximation. IEEE Trans. Industr. Electron. 59, 1238–1247 (2012)

    Article  Google Scholar 

  65. 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 

  66. 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 

  67. Sugeno, M., Yasukawa, T.: A fuzzy logic based approach to qualitative modeling. IEEE Trans. Fuzzy Syst. 1, 7–31 (1993)

    Article  Google Scholar 

  68. Sugiyama, H.: Pulsed power network based on decentralized intelligence for reliable and lowloss electrical power distribution. J. Artif. Intell. Soft Comput. Res. 5(2), 97–108 (2015)

    Article  Google Scholar 

  69. Szczypta, J., Przybył, A., Cpałka, K.: Some aspects of evolutionary designing optimal controllers. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 91–100. Springer, Heidelberg (2013)

    Google Scholar 

  70. Szczypta, J., Przybył, A., Wang, L.: Evolutionary approach with multiple quality criteria for controller design. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014, Part I. LNCS, vol. 8467, pp. 455–467. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  71. Takagi, T., Sugeno, M.: Fuzzy identification of systems and its application to modeling and control. IEEE Trans. Syst. Man Cybern. 15(1), 116–132 (1985)

    Article  MATH  Google Scholar 

  72. Tsakonas, A.: Local and global optimization for Takagi-Sugeno fuzzy system by memetic genetic programming. Expert Syst. Appl. 40(8), 3282–3298 (2013)

    Article  Google Scholar 

  73. 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 

  74. Thiagarajan, R., Rahman, M., Gossink, D., Calbert, G.: A data mining approach to improve military demand forecasting. J. Artif. Intell. Soft Comput. Res. 4(3), 205–214 (2014)

    Article  Google Scholar 

  75. 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 

  76. Yeh, I.C.: Modeling of strength of high performance concrete using artificial neural networks. Cem. Concr. Res. 28(12), 1797–1808 (1998)

    Article  Google Scholar 

  77. Wang, G., Zhang, S.: ABM with behavioral bias and applications in simulating China stock market. J. Artif. Intell. Soft Comput. Res. 5(4), 257–270 (2015)

    Article  Google Scholar 

  78. Wang, Z., Zhang-Westmant, L.: New ranking method for fuzzy numbers by their expansion center. J. Artif. Intell. Soft Comput. Res. 4(3), 181–187 (2014)

    Article  Google Scholar 

  79. Zalasiński, M., Cpałka, K.: A New Method of On-Line Signature Verification Using a Flexible Fuzzy One-Class Classifier. Academic Publishing House EXIT, Warsaw (2011)

    Google Scholar 

  80. 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 

  81. Zalasiński, M., Cpałka, K.: New approach for the on-line signature verification based on method of horizontal partitioning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2013, Part II. LNCS, vol. 7895, pp. 342–350. Springer, Heidelberg (2013)

    Chapter  Google Scholar 

  82. 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 

  83. 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 

  84. 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 

  85. 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 

  86. 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 

  87. 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 

  88. Zhao, W., Lun, R., Espy, D., Reinthal, A.M.: Realtime motion assessment for rehabilitation exercises: integration of kinematic modeling with fuzzy inference. J. Artif. Intell. Soft. Comput. Res. 4(4), 267–285 (2014)

    Article  Google Scholar 

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Acknowledgment

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

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Authors and Affiliations

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

    Łukasz Bartczuk & Krystian Łapa

  2. Institute of Information and Communication Technologies, Bulgarian Academy of Sciences, Sofia, Bulgaria

    Petia Koprinkova-Hristova

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  1. Łukasz Bartczuk
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  2. Krystian Łapa
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  3. Petia Koprinkova-Hristova
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Correspondence to Łukasz Bartczuk .

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Editors and Affiliations

  1. Częstochowa University of Technology, Czestochowa, Poland

    Leszek Rutkowski

  2. Częstochowa University of Technology, Czestochowa, Poland

    Marcin Korytkowski

  3. Częstochowa University of Technology, Czestochowa, Poland

    Rafał Scherer

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

    Ryszard Tadeusiewicz

  5. University of California, Berkeley, California, USA

    Lotfi A. Zadeh

  6. University of Louisville, Louisville, Kentucky, USA

    Jacek M. Zurada

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Bartczuk, Ł., Łapa, K., Koprinkova-Hristova, P. (2016). A New Method for Generating of Fuzzy Rules for the Nonlinear Modelling Based on Semantic Genetic Programming. 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_23

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