Cite

[1] Z. Bankó, A. János, Correlation based dynamic time warping of multivariate time series, Expert Systems with Applications, 39, 12814–12823, 2012.10.1016/j.eswa.2012.05.012Search in Google Scholar

[2] Ł. Bartczuk, A. Przybył, K. Cpałka, A new approach to nonlinear modelling of dynamic systems based on fuzzy rules, International Journal of Applied Mathematics and Computer Science (AMCS), 26, 3, 603-621, 2016.10.1515/amcs-2016-0042Search in Google Scholar

[3] K. Cpałka, Design of Interpretable Fuzzy Systems. Springer, Cham, 2017.10.1007/978-3-319-52881-6Search in Google Scholar

[4] K. Cpałka, K. Łapa, A. Przybył, M. Zalasiński, A new method for designing neuro-fuzzy systems for nonlinear modelling with interpretability aspects, Neurocomputing, 135, 203–217, 2014.10.1016/j.neucom.2013.12.031Search in Google Scholar

[5] K. Cpałka, O. Rebrova, R. Nowicki, L. Rutkowski, On Design of Flexible Neuro-Fuzzy Systems for Nonlinear Modelling, International Journal of General Systems, 42, 706–720, 2013.10.1080/03081079.2013.798912Search in Google Scholar

[6] K. Cpałka, M. Zalasiński, On-line signature verification using vertical signature partitioning, Expert Systems with Applications, 41, 4170–4180, 2014.10.1016/j.eswa.2013.12.047Search in Google Scholar

[7] K. Cpałka, M. Zalasiński, and L. Rutkowski, A new algorithm for identity verification based on the analysis of a handwritten dynamic signature. Applied Soft Computing, 43, 47–56, 2016.10.1016/j.asoc.2016.02.017Search in Google Scholar

[8] K. Cpałka, M. Zalasiński, L. Rutkowski, New method for the on-line signature verification based on horizontal partitioning, Pattern Recognition, 47, 2652–2661, 2014.10.1016/j.patcog.2014.02.012Search in Google Scholar

[9] M. Faúndez-Zanuy, J.M. Pascual-Gaspar, Efficient on-line signature recognition based on multi-section vector quantization, Formal Pattern Analysis & Applications, 14, 37–45, 2011.10.1007/s10044-010-0176-8Search in Google Scholar

[10] Md.M. Ferdaus, S.G. Anavatti, M.A. Garratt, M. Pratama, Development of C-Means Clustering Based Adaptive Fuzzy Controller for a Flapping Wing Micro Air Vehicle, Journal of Artificial Intelligence and Soft Computing Research, 9, 99–109, 2019.10.2478/jaiscr-2018-0027Search in Google Scholar

[11] J. Fernandez de Canetea, A. Garcia-Cerezoa, I. Garcia-Morala, P. Del Saza, E. Ochoa, Correlation based dynamic time warping of multivariate time series, Expert Systems with Applications, 40, 5648–5660, 2013.10.1016/j.eswa.2013.04.012Search in Google Scholar

[12] J. Fierrez, J. Ortega-Garcia, D. Ramos, J. Gonzalez-Rodriguez, HMM–based on-line signature verification: Feature extraction and signature modeling, Pattern Recognition Letters, 28, 2325–2334, 2007.10.1016/j.patrec.2007.07.012Search in Google Scholar

[13] Homepage of Association BioSecure. [Online] Available from: http://biosecure.it-sudparis.eu [Accessed: 21 February 2020].Search in Google Scholar

[14] S. Das, P.N. Suganthan, Differential evolution: A survey of the state-of-the-art. IEEE transactions on evolutionary computation, 15(1), 4-31, 2010.10.1109/TEVC.2010.2059031Search in Google Scholar

[15] N. Houmani, A. Mayoue, S. Garcia-Salicetti, B. Dorizzi, M.I. Khalil, M.N. Moustafa, H. Abbas, D. Muramatsu, B. Yanikoglu, A. Kholmatov, M. Martinez-Diaz, J. Fierrez, J. Ortega-Garcia, J. Roure Alcobe, J. Fabregas, M. Faundez-Zanuy, J.M. Pascual-Gaspar, V. Cardenoso-Payo, C. Vivaracho-Pascual, BioSecure signature evaluation campaign (BSEC’2009): Evaluating online signature algorithms depending on the quality of signatures, Pattern Recognition, 45, 993-1003, 2012.10.1016/j.patcog.2011.08.008Search in Google Scholar

[16] K. Huang, Y. Hong, Stability and style-variation modeling for on–line signature verification, Pattern Recognition, 36, 2253–2270, 2003.10.1016/S0031-3203(03)00126-2Search in Google Scholar

[17] M.T. Ibrahim, M.A. Khan, K.S. Alimgeer, M.K. Khan, I.A. Taj, L. Guan, Velocity and pressure-based partitions of horizontal and vertical trajectories for on-line signature verification, Pattern Recognition, 43, 2817–2832, 2010.10.1016/j.patcog.2010.02.011Search in Google Scholar

[18] S. Ikeda, E. Ooka, Application of differential evolution-based constrained optimization methods to district energy optimization and comparison with dynamic programming, Applied Energy, 254, 113670, 2019.10.1016/j.apenergy.2019.113670Search in Google Scholar

[19] A.K. Jain, F.D. Griess, S.D. Connell, On-line signature verification, Pattern Recognition, 35, 2963–2972, 2002.10.1016/S0031-3203(01)00240-0Search in Google Scholar

[20] A.K. Jain, A. Ross, Introduction to Biometrics, In: A.K. Jain, P. Flynn, A.A. Ross (Eds.), Handbook of Biometrics, Springer, Berlin, Heidelberg, 2008.10.1007/978-0-387-71041-9Search in Google Scholar

[21] M.A.U. Khan, M.K. Khan, M.A. Khan, Velocity-image model for online signature verification, IEEE Trans. Image Process, 15, 3540–3549, 2006.10.1109/TIP.2006.877517Search in Google Scholar

[22] H. Lei, V. Govindaraju, A comparative study on the consistency of features in on-line signature verification, Pattern Recognition Letters, 26, 2483–2489, 2005.10.1016/j.patrec.2005.05.005Search in Google Scholar

[23] S. Li, W. Gong, X. Yan, Ch. Hu, D. Bai, L. Wang, Parameter estimation of photovoltaic models with memetic adaptive differential evolution, Solar Energy, 15, 465–474, 2019.10.1016/j.solener.2019.08.022Search in Google Scholar

[24] K. Łapa, K. Cpałka, L. Wang, New method for design of fuzzy systems for nonlinear modelling using different criteria of interpretability, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 8467, Springer, 217-232, 2014.10.1007/978-3-319-07173-2_20Search in Google Scholar

[25] Ch. O’Reilly, R. Plamondon, Development of a Sigma–Lognormal representation for on-line signatures, Pattern Recognition, 42, 3324–3337, 2009.10.1016/j.patcog.2008.10.017Search in Google Scholar

[26] J.M. Pascual–Gaspar, M. Faúndez–Zanuy, C. Vivaracho, Fast on-line signature recognition based on VQ with time modelling, Engineering Applications of Artificial Intelligence, 24, 368–377, 2011.10.1016/j.engappai.2010.10.015Search in Google Scholar

[27] M.E.H. Pedersen, Good parameters for differential evolution. Hvass Laboratories Technical Report, HL1002, 2010.Search in Google Scholar

[28] L. Rutkowski, Computational Intelligence, Springer, Berlin, Heidelberg, 2008.10.1007/978-3-540-76288-1Search in Google Scholar

[29] L. Rutkowski, K. Cpałka, Flexible neuro-fuzzy systems, IEEE Trans. Neural Networks, 14, 554–574, 2003.10.1109/TNN.2003.81169818238039Search in Google Scholar

[30] L. Rutkowski, K. Cpałka, Designing and learning of adjustable quasi triangular norms with applications to neuro-fuzzy systems, IEEE Trans. Fuzzy Systems, 13, 140–151, 2005.10.1109/TFUZZ.2004.836069Search in Google Scholar

[31] L. Rutkowski, A. Przybył, K. Cpałka, Novel Online Speed Profile Generation for Industrial Machine Tool Based on Flexible Neuro-Fuzzy Approximation, IEEE Trans. Industrial Electronics, 59, 1238–1247, 2012.10.1109/TIE.2011.2161652Search in Google Scholar

[32] R. Storn, K. Price, Differential Evolution – A Simple and Efficient Heuristic for global Optimization over Continuous Spaces, Journal of Global Optimization, 11, 341–359, 1997.10.1023/A:1008202821328Search in Google Scholar

[33] I. Svalina, V. Galzina, R. Lujić, G. Šimunović, Correlation based dynamic time warping of multivariate time series, Expert Systems with Applications, 40, 6055–6063, 2013.10.1016/j.eswa.2013.05.029Search in Google Scholar

[34] J. Szczypta, A. Przybył, K. Cpałka, Some aspects of evolutionary designing optimal controllers, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 7895, Springer, 91-100, 2013.10.1007/978-3-642-38610-7_9Search in Google Scholar

[35] R. Xu, J. Xu, D.C. Wunsch II, A Comparison Study of Validity Indices on Swarm-Intelligence-Based Clustering, IEEE Transactions On Systems, Man, And Cybernetics—Part B: Cybernetics, vol. 42, no. 4, 1243–1256, 2012.10.1109/TSMCB.2012.2188509Search in Google Scholar

[36] D.Y. Yeung, SVC2004: First International Signature Verification Competition, Proc. of ICBA, Springer LNCS-3072, 16–22, 2004.10.1007/978-3-540-25948-0_3Search in Google Scholar

[37] L.A. Zadeh, Fuzzy Sets, Information and Control, 8, 338–353, 1965.10.1016/S0019-9958(65)90241-XSearch in Google Scholar

[38] M. Zalasiński and K. Cpałka, A new method for signature verification based on selection of the most important partitions of the dynamic signature. Neurocomputing, 289, 13-22, 2018.10.1016/j.neucom.2018.02.017Search in Google Scholar

[39] M. Zalasiński, K. Cpałka, New algorithm for online signature verification using characteristic hybrid partitions, Information Systems Architecture and Technology: Proceedings of 36th International Conference on Information Systems Architecture and Technology – ISAT 2015 – Part IV, Advances in Intelligent Systems and Computing, 432, Springer, 147-157, 2016.10.1007/978-3-319-28567-2_13Search in Google Scholar

[40] M. Zalasiński, K. Cpałka, New Approach for the On-Line Signature Verification Based on Method of Horizontal Partitioning, Lecture Notes In Artificial Intelligence, 7895, 342–350, 2013.10.1007/978-3-642-38610-7_32Search in Google Scholar

[41] M. Zalasiński, K. Cpałka, Novel algorithm for the on-line signature verification using selected discretization points groups, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 7894, Springer, 493-502, 2013.10.1007/978-3-642-38658-9_44Search in Google Scholar

[42] M. Zalasiński, K. Cpałka, Novel algorithm for the on-line signature verification, Lecture Notes in Artificial Intelligence 7268, 362–367, 2012.10.1007/978-3-642-29350-4_44Search in Google Scholar

[43] M. Zalasiński, K. Cpałka, Y. Hayashi, New fast algorithm for the dynamic signature verification using global features values, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 9120, Springer, 175-188, 2015.10.1007/978-3-319-19369-4_17Search in Google Scholar

[44] M. Zalasiński, K. Cpałka, E. Rakus-Andersson, An idea of the dynamic signature verification based on a hybrid approach, Artificial Intelligence and Soft Computing, Lecture Notes in Computer Science, 9693, Springer, 232-246, 2016.10.1007/978-3-319-39384-1_21Search in Google Scholar

[45] G. Zhang, K. Xing, Differential evolution meta-heuristics for distributed limited-buffer flowshop scheduling with makespan criterion, Computers & Operations Research, 108, 33–43, 2019.10.1016/j.cor.2019.04.002Search in Google Scholar

[46] Y. Zhao, Q. Liu, A Continuous-Time Distributed Algorithm for Solving a Class of Decomposable Nonconvex Quadratic Programming, Journal of Artificial Intelligence and Soft Computing Research, 8, 283–291, 2018.10.1515/jaiscr-2018-0018Search in Google Scholar

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