Skip to main content
SpringerLink
Log in
Book cover

International Conference on Artificial Intelligence and Soft Computing

ICAISC 2017: Artificial Intelligence and Soft Computing pp 292–307Cite as

Fuzzy PID Controllers with FIR Filtering and a Method for Their Construction

  • Conference paper
  • First Online:

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

Abstract

In this paper a new structure of fuzzy PID controllers with FIR filters and a method for selecting its parameters is presented. The proposed solution can be particularly important in solving problems with noise of the object’s feedback signals. To confirm the effectiveness of the proposed method a typical control problem was tested.

This is a preview of subscription content, 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   39.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
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

Learn about institutional subscriptions

References

  1. Abbas, J.: The bipolar choquet integrals based on ternary-element sets. J. Artif. Intell. Soft Comput. Res. 6(1), 13–21 (2016)

    Article  Google Scholar 

  2. Alia, M.A.K., Younes, T.M., Alsabbah, S.A.: A design of a PID self-tuning controller using LabVIEW. J. Softw. Eng. Appl. 4, 161–171 (2011)

    Article  Google Scholar 

  3. Bartczuk, Ł., Przybył, A., Koprinkova-Hristova, P.: New method for non-linear correction modelling of dynamic objects with genetic programming. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9120, pp. 318–329. Springer, Cham (2015). doi:10.1007/978-3-319-19369-4_29

    Chapter  Google Scholar 

  4. 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 

  5. Bartczuk, Ł., Łapa, K., Koprinkova-Hristova, P.: 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.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 262–278. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_23

    Google Scholar 

  6. Bartczuk, Ł., Galushkin, A.I.: A new method for generating nonlinear correction models of dynamic objects based on semantic genetic programming. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 249–261. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_22

    Google Scholar 

  7. Bilski, J., Rutkowski, L.: Numerically robust learning algorithms for feed forward neural networks. In: Advances in Soft Computing-Neural Networks and Soft Computing, pp. 149–154. Physica-Verlag, A Springer-Verlag Company (2003)

    Google Scholar 

  8. Bilski, J., Smola̧g, J.: Parallel realisation of the recurrent RTRN neural network learning. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2008. LNCS, vol. 5097, pp. 11–16. Springer, Heidelberg (2008). doi:10.1007/978-3-540-69731-2_2

    Chapter  Google Scholar 

  9. Bilski, J., Smola̧g, J.: Parallel realisation of the recurrent elman neural network learning. In: Rutkowski, L., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2010. LNCS, vol. 6114, pp. 19–25. Springer, Heidelberg (2010). doi:10.1007/978-3-642-13232-2_3

    Chapter  Google Scholar 

  10. Bilski, J., Smoląg, J.: Parallel realisation of the recurrent multi layer perceptron learning. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7267, pp. 12–20. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29347-4_2

    Chapter  Google Scholar 

  11. Bilski, J., Smoląg, J., Galushkin, A.I.: The parallel approach to the conjugate gradient learning algorithm for the feedforward neural networks. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 12–21. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_2

    Chapter  Google Scholar 

  12. Boyd, S., Hast, M., Åström, K.J.: MIMO PID tuning via iterated LMI restriction. Int. J. Robust Nonlinear Control 26, 1718–1731 (2016)

    Article  MathSciNet  MATH  Google Scholar 

  13. Brester, C., Semenkin, E., Sidorov, M.: Multi-objective heuristic feature selection for speech-based multilingual emotion recognition. J. Artif. Intell. Soft Comput. Res. 6(4), 243–253 (2016)

    Article  Google Scholar 

  14. 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 

  15. Cheng, S., Li, C.W.: Fuzzy PDFF-IIR controller for PMSM drive systems. Control Eng. Pract. 19, 828–835 (2011)

    Article  Google Scholar 

  16. Cierniak, R., Rutkowski, L.: On image compression by competitive neural networks and optimal linear predictors. Sig. Process. Image Commun. 156, 559–565 (2000)

    Article  Google Scholar 

  17. Cpałka, K.: Design of Interpretable Fuzzy Systems. Springer, Heidelberg (2017)

    Google Scholar 

  18. 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 

  19. 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 

  20. 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 

  21. 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 

  22. Duda, P., Jaworski, M., Pietruczuk, L.: On pre-processing algorithms for data stream. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7268, pp. 56–63. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29350-4_7

    Chapter  Google Scholar 

  23. Er, M.J., Duda, P.: On the weak convergence of the orthogonal series-type kernel regresion neural networks in a non-stationary environment. In: Wyrzykowski, R., Dongarra, J., Karczewski, K., Waśniewski, J. (eds.) PPAM 2011. LNCS, vol. 7203, pp. 443–450. Springer, Heidelberg (2012). doi:10.1007/978-3-642-31464-3_45

    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.) ICAISC 2015. LNCS, vol. 9120, pp. 364–378. Springer, Cham (2015). doi:10.1007/978-3-319-19369-4_33

    Chapter  Google Scholar 

  25. Dziwiński, P., Avedyan, E.D.: A new approach for using the fuzzy decision trees for the detection of the significant operating points in the nonlinear modeling. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 279–292. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_24

    Google Scholar 

  26. Gabryel, M.: A bag-of-features algorithm for applications using a NoSQL database. In: Dregvaite, G., Damasevicius, R. (eds.) ICIST 2016. CCIS, vol. 639, pp. 332–343. Springer, Cham (2016). doi:10.1007/978-3-319-46254-7_26

    Chapter  Google Scholar 

  27. Gabryel, M., Cpałka, K., Rutkowski, L.: Evolutionary strategies for learning of neuro-fuzzy systems. In: Proceedings of the I Workshop on Genetic Fuzzy Systems, Granada, pp. 119–123 (2005)

    Google Scholar 

  28. Gabryel, M., Grycuk, R., Korytkowski, M., Holotyak, T.: Image indexing and retrieval using GSOM algorithm. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 706–714. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_63

    Chapter  Google Scholar 

  29. Gabryel, M.: The bag-of-features algorithm for practical applications using the MySQL database. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 635–646. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_56

    Google Scholar 

  30. Gałkowski, T., Rutkowski, L.: Nonparametric fitting of multivariate functions. IEEE Trans. Autom. Control 31(8), 785–787 (1986)

    Article  MATH  Google Scholar 

  31. Hagan, M.T., Demuth, H.B., Jesús, O.D.: An introduction to the use of neural networks in control systems. Int. J. Robust Nonlinear Control 12(11), 959–985 (2002)

    Article  MATH  Google Scholar 

  32. Hayashi, Y., Tanaka, Y., Takagi, T., Saito, T., Iiduka, H., Kikuchi, H., Bologna, G.: Recursive-rule extraction algorithm with J48graft and applications to generating credit scores. J. Artif. Intell. Soft Comput. Res. 6(1), 35–44 (2016)

    Article  Google Scholar 

  33. Held, P., Dockhorn, A., Kruse, R.: On merging and dividing social graphs. J. Artif. Intell. Soft Comput. Res. 5(1), 23–49 (2015)

    Article  Google Scholar 

  34. Jaworski, M., Er, M.J., Pietruczuk, L.: On the application of the parzen-type kernel regression neural network and order statistics for learning in a non-stationary environment. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2012. LNCS, vol. 7267, pp. 90–98. Springer, Heidelberg (2012). doi:10.1007/978-3-642-29347-4_11

    Chapter  Google Scholar 

  35. Kapustianyk, V., Shchur, Y., Kityk, I., Rudyk, V., Lach, G., Laskowski, Ł., Tkaczyk, S., Swiatek, J., Davydov, V.: Resonance dielectric dispersion of TEA-CoCl2Br 2 nanocrystals incorporated into the PMMA matrix. J. Phys. Condens. Matter 20(36), 365215–365223 (2008). IOP Publishing

    Article  Google Scholar 

  36. Klement, E.P., Mesiar, R., Pap, E.: Triangular Norms. Springer, Heidelberg (2000)

    Book  MATH  Google Scholar 

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

    Article  MathSciNet  Google Scholar 

  38. Kurien, M.: Overview of different approach of PID controller tuning. Int. J. Res. Advent Technol. 2(1), 167–175 (2014)

    Google Scholar 

  39. Lan, K., Sekiyama, K.: Autonomous viewpoint selection of robot based on aesthetic evaluation of a scene. J. Artif. Intelli. Soft Comput. Res. 6(4), 255–265 (2016)

    Article  Google Scholar 

  40. Laskowska, M., Laskowski, Ł., Jelonkiewicz, J.: SBA-15 mesoporous silica activated by metal ions-verification of molecular structure on the basis of Raman spectroscopy supported by numerical simulations. J. Mol. Struct. 1100, 21–26 (2015). Elsevier

    Article  Google Scholar 

  41. 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 

  42. Laskowski, Ł., Laskowska, M., Jelonkiewicz, J., Boullanger, A.: Spin-glass implementation of a hopfield neural structure. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 89–96. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_9

    Chapter  Google Scholar 

  43. Laskowski, Ł., Laskowska, M., Jelonkiewicz, J., Boullanger, A.: Molecular approach to hopfield neural network. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2015. LNCS, vol. 9119, pp. 72–78. Springer, Cham (2015). doi:10.1007/978-3-319-19324-3_7

    Chapter  Google Scholar 

  44. Laskowski, Ł., Laskowska, M., Jelonkiewicz, J., Dulski, M., Wojtyniak, M., Fitta, M., Balanda, M.: SBA-15 mesoporous silica free-standing thin films containing copper ions bounded via propyl phosphonate units-preparation and characterization. J. Solid State Chem. 241, 143–151 (2016). Elsevier

    Article  Google Scholar 

  45. Laskowski, Ł., Laskowska, M., Jelonkiewicz, J., Gałkowski, T., Pawlik, P., Piech, H., Doskocz, M.: Iron doped SBA-15 mesoporous silica studied by Mössbauer spectroscopy. J. Nanomaterials 2016, 1–6 (2016). Hindawi Publishing Corp

    Article  Google Scholar 

  46. Leva, A., Papadopoulos, A.V.: Tuning of event-based industrial controllers with simple stability guarantees. J. Process Control 23, 1251–1260 (2013)

    Article  Google Scholar 

  47. Li, X., Er, M.J., Lim, B.S., Zhou, J.H., Gan, O.P., Rutkowski, L.: Fuzzy regression modeling for tool performance prediction and degradation detection. Int. J. Neural Syst. 2005, 405–419 (2010)

    Article  Google Scholar 

  48. 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 

  49. Łapa, K., Przybył, A., Cpałka, K.: A new approach to designing interpretable models of dynamic systems. Artif. Intell. Soft Comput. 7895, 523–534 (2013)

    Article  Google Scholar 

  50. Łapa, K., Szczypta, J., Venkatesan, R.: Aspects of structure and parameters selection of control systems using selected multi-population algorithms. Artif. Intell. Soft Comput. 9120, 247–260 (2015)

    Article  Google Scholar 

  51. Łapa, K., Szczypta, J., Saito, T.: Aspects of evolutionary construction of new flexible PID-fuzzy controller. Artif. Intell. Soft Comput. 9692, 450–464 (2016)

    Google Scholar 

  52. Maggio, M., Bonvini, M., Leva, A.: The PID+p controller structure and its contextual autotuning. J. Process Control 22, 1237–1245 (2012)

    Article  Google Scholar 

  53. Melanie, M.: An Introduction to Genetic Algorithms. MIT Press, Massachusetts (1999)

    MATH  Google Scholar 

  54. Nobukawa, S., Nishimura, H., Yamanishi, T., Liu, J.: Chaotic states induced by resetting process in izhikevich neuron model. J. Artif. Intell. Soft Comput. Res. 5(2), 109–119 (2015)

    Article  Google Scholar 

  55. Pamar, K., Arvapalli, R., Sadhu, Y., Viswaraju, S.: Cascaded PID controller design for heating furnace temperature control. IOSR J. Electr. Commun. Eng. 5(3), 76–83 (2013)

    Article  Google Scholar 

  56. Ribića, A.I., Mataušek, M.R.: A dead-time compensating PID controller structure and robust tuning. J. Process Control 22, 1340–1349 (2012)

    Article  Google Scholar 

  57. Rivero, C.R., Pucheta, J., Laboret, S., Sauchelli, V., Patio, D.: Energy associated tuning method for short-term series forecasting by complete and incomplete datasets. J. Artif. Intell. Soft Comput. Res. 7(1), 5–16 (2017)

    Article  Google Scholar 

  58. Rutkowski, L.: On-line identification of time-varying systems by nonparametric techniques. IEEE Trans. Autom. Control 27(1), 228–230 (1982)

    Article  MATH  Google Scholar 

  59. Rutkowski, L.: On nonparametric identification with prediction of time-varying systems. IEEE Trans. Autom. Control 29(1), 58–60 (1984)

    Article  MATH  Google Scholar 

  60. Rutkowski, L.: Nonparametric identification of quasi-stationary systems. Syst. Control Lett. 6(1), 33–35 (1985)

    Article  MATH  Google Scholar 

  61. 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 

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

    Article  Google Scholar 

  63. Rutkowski, L.: Computational Intelligence. Springer, Heidelberg (2008)

    Book  MATH  Google Scholar 

  64. 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 

  65. Rutkowski, L., Cpałka, K.:, Flexible weighted neuro-fuzzy systems. In: Proceedings of the 9th International Conference on Neural Information Processing (ICONIP 2002), Orchid Country Club, Singapore, November 18–22, 2002, CD (2002)

    Google Scholar 

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

    Google Scholar 

  67. Rutkowski, L., Przybył, 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 

  68. 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 

  69. Saitoh, D., Hara, K.: Mutual learning using nonlinear perceptron. J. Artif. Intell. Soft Comput. Res. 5(1), 71–77 (2015)

    Article  Google Scholar 

  70. Sakurai, S., Nishizawa, M., Soft, C.R.: A new approach for discovering top-k sequential patterns based on the variety of items. J. Artif. Intell. Soft Comput. Res. 5(2), 141–153 (2015)

    Article  Google Scholar 

  71. Segovia, R.V., Hägglund, T., Aström, K.J.: Noise filtering in PI and PID control. In: American Control Conference, pp. 1763–1770 (2013)

    Google Scholar 

  72. Szczypta, J., Łapa, K., Shao, Z.: Aspects of the selection of the structure and parameters of controllers using selected population based algorithms. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2014. LNCS, vol. 8467, pp. 440–454. Springer, Cham (2014). doi:10.1007/978-3-319-07173-2_38

    Chapter  Google Scholar 

  73. Tabellout, M., Kassiba, A., Tkaczyk, S., Laskowski, Ł., Świątek, J.: Dielectric and EPR investigations of stoichiometry and interface effects in silicon carbide nanoparticles. J. Phys. Condens. Matter 18(4), 11–43 (2006). IOP Publishing

    Article  Google Scholar 

  74. Tezuka, T., Claramunt, C.: Kernel analysis for estimating the connectivity of a network with event sequences. J. Artif. Intell. Soft Comput. Res. 7(1), 17–31 (2017)

    Article  Google Scholar 

  75. Yamamoto, Y., Yoshikawa, T., Furuhashi, T.: Improvement of performance of Japanese P300 speller by using second display. J. Artif. Intell. Soft Comput. Res. 5(3), 221–226 (2015)

    Article  Google Scholar 

  76. Zalasiński, M., Cpałka, K.: A new method of on-line signature verification using a flexible fuzzy one-class classifier, pp. 38–53. Academic Publishing House EXIT (2011)

    Google Scholar 

  77. 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. LNCS, vol. 7894, pp. 493–502. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38658-9_44

    Chapter  Google Scholar 

  78. 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. LNCS, vol. 8468, pp. 216–230. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_20

    Chapter  Google Scholar 

  79. 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. LNCS, vol. 8468, pp. 231–245. Springer, Cham (2014). doi:10.1007/978-3-319-07176-3_21

    Chapter  Google Scholar 

  80. Zalasiński, M., Cpałka, K., Hayashi, Y.: A new approach to the dynamic signature verification aimed at minimizing the number of global features. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds.) ICAISC 2016. LNCS, vol. 9693, pp. 218–231. Springer, Cham (2016). doi:10.1007/978-3-319-39384-1_20

    Google Scholar 

  81. 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 

  82. 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. LNCS, vol. 7895, pp. 113–121. Springer, Heidelberg (2013). doi:10.1007/978-3-642-38610-7_11

    Chapter  Google Scholar 

Download references

Acknowledgment

The project was financed by the National Science Centre (Poland) 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

    Krystian Łapa, Krzysztof Cpałka & Andrzej Przybył

  2. Department of Computer Science, Meiji University, Tokyo, Japan

    Takamichi Saito

Authors
  1. Krystian Łapa
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Krzysztof Cpałka
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Andrzej Przybył
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Takamichi Saito
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Krystian Łapa .

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

Łapa, K., Cpałka, K., Przybył, A., Saito, T. (2017). Fuzzy PID Controllers with FIR Filtering and a Method for Their Construction. 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_27

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-59060-8_27

  • 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

Search

Navigation