参考文献:1.Abdullah, I.A.O., Ayman, A.A.L.: The advantages of PID fuzzy controllers over the conventional types. Am. J. Appl. Sci. 5(6), 653–658 (2008)CrossRef 2.Alonso, J.M., Magdalena, L.: HILK++: an interpretability-guided fuzzy modeling methodology for learning readable and comprehensible fuzzy rule-based classifiers. Soft. Comput. 15(10), 1959–1980 (2011)CrossRef 3.Bartczuk, Ł.: Gene expression programming in correction modelling of nonlinear dynamic objects. In: Borzemski, L., Grzech, A., Świa̧tek, 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, Switzerland (2016) 4.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)CrossRef 5.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)CrossRef 6.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)CrossRef 7.Boiko, I.: Variable-structure PID controller for level process. Control Eng. Pract. 21(5), 700–707 (2013)MathSciNet CrossRef 8.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)CrossRef 9.Cpałka, K.: On evolutionary designing and learning of flexible neuro-fuzzy structures for nonlinear classification. Nonlinear Anal. A: Theor. Meth. Appl. 71, 1659–1672 (2009)CrossRef 10.Cpałka, K.: A new method for design and reduction of neuro-fuzzy classification systems. IEEE Trans. Neural Netw. 20, 701–714 (2009)CrossRef 11.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) 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)CrossRef 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)CrossRef MATH 14.Cpałka K., Rutkowski L.: Flexible Takagi-Sugeno fuzzy systems. In: Proceedings of the International Joint Conference on Neural Networks, pp. 1764–1769. Montreal (2005) 15.Cpałka, K., Rutkowski, L.: Flexible Takagi-Sugeno neuro-fuzzy structures for nonlinear approximation. WSEAS Trans. Syst. 4(9), 1450–1458 (2005) 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), pp. 8510–8516. IEEE, Vancouver (2006) 17.Cpałka, K., Zalasiński, M.: On-line signature verification using vertical signature partitioning. Expert Syst. Appl. 41(9), 4170–4180 (2014)CrossRef 18.Cpałka, K., Zalasiński, M., Rutkowski, L.: New method for the on-line signature verification based on horizontal partitioning. Pattern Recogn. 47, 2652–2661 (2014)CrossRef 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 CrossRef 20.Duch W., Korbicz J., Rutkowski L., Tadeusiewicz R. (eds.), Biocybernetics and Biomedical Engineering 2000. Neural Networks, Akademicka Oficyna Wydawnicza, EXIT, Warsaw 2000, vol. 6 (in Polish) (2000) 21.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, Heidelberg (2015)CrossRef 22.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)CrossRef 23.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)CrossRef 24.Gacto, M.J., Alcalá, R., Herrera, F.: Interpretability of linguistic fuzzy rule-based systems: an overview of interpretability measures. Inf. Sci. 181(20), 4340–4360 (2011)CrossRef MATH 25.Gałkowski, T., Rutkowski, L.: Nonparametric recovery of multivariate functions with applications to system identification. Proc. IEEE 73(5), 942–943 (1985)CrossRef 26.Radu, V.: Stochastic Modeling of Thermal Fatigue Crack Growth. ACM, vol. 1. Springer, Heidelberg (2015) 27.Hayashi, Y., Tanaka, Y., Takagi, T., Saito, T., Iiduka, H., Kikuchi, H., Bologna, G., Mitra, S.: Recursive-rule extraction algorithm with j48graft and applications to generating credit scores. J. Artif. Intell. Soft Comput. Res. 6(1), 35–44 (2016)CrossRef 28.Ishibuchi, H., Nakashima, T., Murata, T.: Comparsion of the Michigan and Pittsburgh approaches to the design of fuzzy classification systems. Electron. Commun. Jpn. 80(12), 379–387 (1997)CrossRef 29.Kasthurirathna, D., Piraveenan, M., Uddin, S.: Evolutionary stable strategies in networked games: the influence of topology. J. Artif. Intell. Soft Comput. Res. 5(2), 83–95 (2015)CrossRef 30.Korytkowski, M., Rutkowski, L., Scherer, R.: Fast image classification by boosting fuzzy classifiers. Inf. Sci. 327, 175–182 (2016)MathSciNet CrossRef 31.Leva, A., Papadopoulos, A.V.: Tuning of event-based industrial controllers with simple stability guarantees. J. Process Control 23, 1251–1260 (2013)CrossRef 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)CrossRef 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)CrossRef 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)CrossRef 35.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.) Artificial Intelligence and Soft Computing. LNCS, vol. 9120, pp. 247–260. Springer, Heidelberg (2015)CrossRef 36.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)CrossRef 37.Maggio, M., Bonvini, M., Leva, A.: The PID+p controller structure and its contextual autotuning. J. Process Control 22, 1237–1245 (2012)CrossRef 38.Liu, C., Sun, F.: Lens distortion correction using ELM. In: Cao, J., Mao, K., Cambria, E., Man, Z., Toh, K.-A. (eds.) Proceedings of ELM-2014 Volume 2, PALO, vol. 4, pp. 21–30. Springer, Heidelberg (2014) 39.Marquez A. A., Marquez F. A., Peregrin A.: A multi-objective evolutionary algorithm with an interpretability improvement mechanism for linguistic fuzzy systems with adaptive defuzzification. In: IEEE International Conference on Fuzzy Systems, pp. 1–7 (2010) 40.Malhotra, R., Sodhi, R.: Boiler flow control using PID and fuzzy logic controller. IJCSET 1(6), 315–319 (2011) 41.Mencar, C., Castiello, C., Cannone, R., Fanelli, A.M.: Interpretability assessment of fuzzy knowledge bases: a cointension based approach. Int. J. Approximate Reasoning 52(4), 501–518 (2011)MathSciNet CrossRef 42.Murata, M., Ito, S., Tokuhisa, M., Ma, Q.: Order estimation of japanese paragraphs by supervised machine learning and various textual features. J. Artif. Intell. Soft Comput. Res. 5(4), 247–255 (2015)CrossRef 43.Natsheh, E., Buragga, K.A.: Comparison between conventional and fuzzy logic PID controllers for controlling DC motors. IJCSI Int. J. Comput. Sci. Issues 7(5), 128–134 (2010) 44.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)CrossRef 45.Ribića, A.I., Mataušek, M.R.: A dead-time compensating PID controller structure and robust tuning. J. Process Control 22, 1340–1349 (2012)CrossRef 46.Rutkowski, L.: Sequential estimates of probability densities by orthogonal series and their application in pattern classification. IEEE Trans. Syst. Man Cybern. 10(12), 918–920 (1980)MathSciNet CrossRef MATH 47.Rutkowski, L.: Nonparametric identification of quasi-stationary systems. Syst. Control Lett. 6(1), 33–35 (1985)MathSciNet CrossRef MATH 48.Rutkowski, L.: Real-time identification of time-varying systems by non-parametric algorithms based on Parzen kernels. Int. J. Syst. Sci. 16(9), 1123–1130 (1985)CrossRef MATH 49.Rutkowski, L.: A general approach for nonparametric fitting of functions and their derivatives with applications to linear circuits identification. IEEE Trans. Circuits Syst. 33(8), 812–818 (1986)CrossRef MATH 50.Rutkowski, L.: Application of multiple Fourier-series to identification of multivariable non-stationary systems. Int. J. Syst. Sci. 20(10), 1993–2002 (1989)MathSciNet CrossRef MATH 51.Rutkowski, L.: Adaptive probabilistic neural networks for pattern classification in time-varying environment. IEEE Trans. Neural Netw. 15(4), 811–827 (2004)MathSciNet CrossRef 52.Rutkowski, L.: Computational Intelligence. Springer, Heidelberg (2008)CrossRef MATH 53.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) 54.Rutkowski L., Cpałka K.: Compromise approach to neuro-fuzzy systems. In: Proceedings of the 2nd Euro-International Symposium on Computational Intelligence, vol. 76, pp. 85–90. Koszyce (2002) 55.Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: Decision trees decision trees for mining data streams based on the Gaussian approximation. IEEE Trans. Knowl. Data Eng. 26(1), 108–119 (2014)CrossRef 56.Rutkowski, L., Jaworski, M., Pietruczuk, L., Duda, P.: The CART decision tree for mining data streams. Inf. Sci. 266, 1–15 (2014)CrossRef 57.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)MathSciNet CrossRef 58.Rutkowski, L., Pietruczuk, L., Duda, P., Jaworski, M.: Decision Trees for mining data streams based on the McDiarmid’s bound. IEEE Trans. Knowl. Data Engi. 25(6), 1272–1279 (2013)CrossRef 59.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)CrossRef 60.Rutkowski, Leszek, Przybył, Andrzej, Cpałka, Krzysztof, Er, Meng Joo: Online speed profile generation for industrial machine tool based on neuro-fuzzy approach. In: Rutkowski, Leszek, Scherer, Rafał, Tadeusiewicz, Ryszard, Zadeh, Lotfi A., Zurada, Jacek M. (eds.) ICAISC 2010, Part II. LNCS, vol. 6114, pp. 645–650. Springer, Heidelberg (2010)CrossRef 61.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)CrossRef 62.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. Advances in Soft Computing, vol. 19, pp. 570–577. Physica, Verlag (2003)CrossRef 63.Starczewski, J.T., Rutkowski, L.: Connectionist structures of type 2 fuzzy inference systems. In: Wyrzykowski, R., Dongarra, J., Paprzycki, M., Waśniewski, J. (eds.) Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science, vol. 2328, pp. 634–642. Springer, Heidelberg (2002)CrossRef 64.Cheng, S., Li, C.-W.: Fuzzy PDFF-IIR controller for PMSM drive systems. Control Eng. Pract. 19, 828–835 (2011)CrossRef 65.Yeomans, J.S.: A parametric testing of the firefly algorithm in the determination of the optimal osmotic drying parameters of mushrooms. J. Artif. Intell. Soft Comput. Res. 4(4), 257–266 (2014)CrossRef 66.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. LNCS, vol. 7268, pp. 362–367. Springer, Heidelberg (2012)CrossRef 67.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)CrossRef 68.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, Heidelberg (2014)CrossRef 69.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.) ICAISC 2015. LNCS, vol. 9120, pp. 161–174. Springer, Heidelberg (2015)CrossRef 70.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, Heidelberg (2014)CrossRef 71.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.) ICAISC 2015. LNCS, vol. 9120, pp. 175–188. Springer, Heidelberg (2015)CrossRef 72.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)CrossRef
作者单位:Krystian Łapa (19) Jacek Szczypta (19) Takamichi Saito (20)
19. Institute of Computational Intelligence, Czȩstochowa University of Technology, Czȩstochowa, Poland 20. Department of Computer Science, Meiji University, Tokyo, Japan
丛书名:Artificial Intelligence and Soft Computing
ISBN:978-3-319-39378-0
刊物类别:Computer Science
刊物主题:Artificial Intelligence and Robotics Computer Communication Networks Software Engineering Data Encryption Database Management Computation by Abstract Devices Algorithm Analysis and Problem Complexity
出版者:Springer Berlin / Heidelberg
ISSN:1611-3349
卷排序:9692
文摘
In this paper a new approach for designing control systems is presented. It is based on ensemble of PID controller and flexible neuro-fuzzy system with dynamic structure. A hybrid population-based algorithm is proposed to select the structure and its parameters. In this hybridization a genetic algorithm is used to select the controller structure and evolutionary strategy is used to simultaneously select the controller parameters. The proposed approach allows design interpretable control systems based on different control criteria and different controlled object. The proposed controller structure and proposed learning algorithm were tested on typical control problem.