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Multivariate fuzzy transform of complex-valued functions determined by monomial basis

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Abstract

In this paper, we introduce the multivariate fuzzy transform of higher degree of complex-valued functions. Apart from the orthogonal bases of multivariate complex polynomials of weighted Hilbert spaces that are derived by the Gram–Schmidt orthogonalization process, which can be problematic and imprecise in certain cases, we propose to compute the multivariate fuzzy transform components using a simple matrix calculus with the help of the monomial bases. By this novel approach, we derive two types of upper bound of the approximation error both of multivariate complex-valued functions and of their partial derivatives (the latter by the multivariate higher degree fuzzy transform). The results are demonstrated on examples.

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Notes

  1. It means that the real part as well as the imaginary part is bounded piecewise continuous real-valued n-variate functions.

  2. Note that a fair comparison of methods is beyond of the scope of this paper, because a suitable choice of the parameters of the respective method can significantly influence the quality of particular approximation. A preliminary comparison of methods with the univariate higher degree F-transform can be found in Holčapek et al. (2016).

References

  • Adali T, Schreier P, Scharf L (2011) Complex-valued signal processing: the proper way to deal with impropriety. IEEE Tras Signal Process 59(11):5101–5125

    Article  MathSciNet  Google Scholar 

  • Astola J, Katkovnik V, Egiazarian K (2006) Local approximation techniques in signal and image processing, vol PM157. SPIE Press, Monograph SPIE Publications, Bellingham

    MATH  Google Scholar 

  • Crouzet JF (2012) Fuzzy projection versus inverse fuzzy transform as sampling/interpolation schemes. Fuzzy Sets Syst 193:108–121

    Article  MathSciNet  MATH  Google Scholar 

  • Di Martino F, Loia V, Perfilieva I, Sessa S (2008) An image coding/decoding method based on direct and inverse fuzzy transforms. Int J Approx Reason 48(1):110–131

    Article  MATH  Google Scholar 

  • Di Martino F, Loia V, Sessa S (2010) Fuzzy transforms method and attribute dependency in data analysis. Inf Sci 180(4):493–505

    Article  MathSciNet  MATH  Google Scholar 

  • Di Martino F, Loia V, Sessa S (2010) A segmentation method for images compressed by fuzzy transforms. Fuzzy Sets Syst 161(1):56–74

    Article  MathSciNet  MATH  Google Scholar 

  • Di Martino F, Loia V, Sessa S (2011) Fuzzy transforms method in prediction data analysis. Fuzzy Sets Syst 180(1):146–163

    Article  MathSciNet  MATH  Google Scholar 

  • Duistermaat J, Kolik J (2004) Multidimensional real analysis I: differentiation. Cambridge University Press, Cambridge

    Book  Google Scholar 

  • Duistermaat J, Kolik J (2004) Multidimensional real analysis II: integration. Cambidge University Press, Cambidge

    Book  Google Scholar 

  • Gaeta M, Loia V, Tomasiello S (2016) Cubic B-spline fuzzy transforms forn and efficient and secure compression in wireless sensor networks. Inf Sci 339:19–30

    Article  Google Scholar 

  • Graba F, Strauss O (2016) An interval-valued inversion of the non-additive interval-valued F-transform: Use for upsampling a signal. Fuzzy Sets Syst 288:26–45

    Article  MathSciNet  Google Scholar 

  • Hodáková P (2015) Fuzzy (F-)transform of functions of two variables and its applications in image processing. Ph.D. thesis, University of Ostrava

  • Hölder W, Müller M, Sperlich S, Werwatz A (2004) Nonparametric and semiparametric models. Springer, Berlin

    MATH  Google Scholar 

  • Holčapek M, Nguyen L (2016) Suppression of high frequencies in time series using fuzzy transform of higher degree. In: Carvalho J, Lesot M, Kaymak U, Vieira S, Bouchon-Meunier B, Yager R (eds) Information processing and management of uncertainty in knowledge-based systems. IPMU 2016, Communications in Computer and Information Science, vol 611. Springer, Berlin, pp 705–716

  • Holčapek, M, Nguyen L (2017) Trend-cycle estimation using fuzzy transform of higher degree. Iran J Fuzzy Syst (in press)

  • Holčapek M, Nguyen L, Tichý T (2016) Polynomial alias higher degree fuzzy transform of complex-valued functions. Submitted to Fuzzy sets and Systems

  • Holčapek M, Novák V, Perfilieva I (2013) Analysis of stationary processes using fuzzy transform. In: Pasi G, Montero J, Ciucci D (eds) Proceedings of the 8th conference of the European society for fuzzy logic and technology (EUSFLAT 2013), Atlantis Press, pp 714–721

  • Holčapek M, Perfilieva I, Novák V, Kreinovich V (2015) Necessary and sufficient conditions for generalized uniform fuzzy partitions. Fuzzy Sets Syst 277:97–121

    Article  MathSciNet  Google Scholar 

  • Holčapek M, Tichý T (2010) A probability density function estimation using F-transform. Kybernetika 46(3):447–458

    MathSciNet  MATH  Google Scholar 

  • Holčapek M, Tichý T (2011) A smoothing filter based on fuzzy transform. Fuzzy Sets Syst 180(1):69–97. doi:10.1016/j.fss.2011.05.028

    Article  MathSciNet  MATH  Google Scholar 

  • Holčapek M, Tichý T (2012) An application of an n-dimensional fuzzy smoothing filter in financial modeling. In: Business engineering and industrial applications colloquium (BEIAC), 2012 IEEE, pp 226–231

  • Holčapek M, Tichý T (2014) Discrete fuzzy transform of higher degree. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ-IEEE), IEEE, pp 604–611

  • Holčapek M, Tichý T (2015) Discrete multivariate F-transform of higher degree. In: Proceedings of IEEE international conference on fuzzy systems (FUZZ-IEEE), IEEE, pp. 1–8

  • Jazwinski AH (2007) Stochastic processes and filtering theory, vol 16. Dover Publications, Mineola, p 376

    MATH  Google Scholar 

  • Khastan A, Alijan Z, Perfilieva I (2016) Fuzzy transform to approximate solution of two-point boundary value problems. Math Methods Appl Sci. doi:10.1002/mma.3832

  • Khastan A, Perfilieva I, Alijan Z (2016) A new fuzzy approximation method to cauchy problems by fuzzy transform. Fuzzy Sets Syst 288:75–79

    Article  MathSciNet  Google Scholar 

  • Kim JD, Moon EL, Jeong E, Hong DH (2016) Generalized uniform fuzzy partition: the solution to Holčapek’s open problem. Fuzzy Sets Syst 297:141–144

    Article  Google Scholar 

  • Klir GJ, Yuan B (1995) Fuzzy sets and fuzzy logic: theory and applications. Prentice Hall, Upper Saddle River

    MATH  Google Scholar 

  • Kriz I, Pultr A (2013) Introduction to mathematical analysis. Springer, Basel

    Book  MATH  Google Scholar 

  • Lee WJ, Jung HY, Yoon JH, Choi SH (2014) Forecasting using F-transform based on bootstrap technique. In: Proceedings of the IEEE international conference on fuzzy systems, pp 513–516

  • Loia V, Tomasiello S, Vaccaro A (2017) Fuzzy transform based compression of electric signal waveform for smart grid. IEEE Trans Fuzzy Syst 47(1):121–132

    Google Scholar 

  • Loia V, Tomasiello S, Vaccaro A (2017) Using fuzzy transform in multi-agent based monitoring of smart grids. Inf Sci 388–389:209–224

    Article  Google Scholar 

  • Nguyen HT, Walker EA (2006) A first course in fuzzy logic. CRC Press, Boca Raton

    MATH  Google Scholar 

  • Novák V, Štěpnička M, Perfilieva I, Pavliska V (2008) Analysis of periodical time series using soft computing methods. In: Ruan D, Montero J, Lu J, Martinéz L, D’hondt P, Kerre EE (eds) Computational intelligence in decision and control, World Scientific, New Jersey, pp 55–60

  • Pagan A, Ullah A (1999) Nonparametric econometrics. Cambridge University Press, New York

    Book  Google Scholar 

  • Patané G (2011) Fuzzy transform and least square approximation: analogies, difference, and generalizations. Fuzzy Sets Syst 180:41–54

    Article  MathSciNet  MATH  Google Scholar 

  • Perfilieva I (2004) Fuzzy transforms. In: Peters J, Skowron A, Dubois D, Grzymała-Busse J, Inuiguchi M, Polkowski L (eds) Transactions on rough sets II, Lecture Notes in Computer Science, vol 3135, Springer, Heidelberg, pp 63–81. doi:10.1007/b100633

  • Perfilieva I (2005) Fuzzy transforms and their applications to image compression. In: Bloch I, Petrosino A, Tettamanzi A (eds) Fuzzy logic and applications, vol 3849. Lecture Notes in Computer Science. Springer, Berlin, pp 19–31

    Chapter  Google Scholar 

  • Perfilieva I (2006) Fuzzy transforms: theory and applications. Fuzzy Sets Syst 157(8):993–1023

    Article  MathSciNet  MATH  Google Scholar 

  • Perfilieva I, Daňková M (2009) Towards F-transform of a higher degree. In: Proceedings of IFSA/EUSFLAT 2009, Lisbon, Portugal, pp 585–588

  • Perfilieva I, Daňková M, Bede B (2011) Towards a higher degree F-transform. Fuzzy Sets Syst 180(1):3–19

    Article  MathSciNet  MATH  Google Scholar 

  • Perfilieva I, Hodáková P, Hurtík P (2016) Differentiation by the F-transform and application to edge detection. Fuzzy Sets Syst 388:96–114

    Article  MathSciNet  MATH  Google Scholar 

  • Perfilieva I, Holčapek M, Kreinovich V (2016) A new reconstruction from F-transform components. Fuzzy Sets Syst 288:3–25

    Article  MathSciNet  Google Scholar 

  • Perfilieva I, Novák V, Dvořák A (2008) Fuzzy transform in the analysis of data. Int J Approx Reason 48(1):36–46

    Article  MATH  Google Scholar 

  • Perfilieva I, Novák V, Pavliska V, Dvořák A, Štěpnička M (2008) Analysis and prediction of time series using fuzzy transform. In: Proceedings of IEEE world congress on computational intelligence, pp 3875–3879

  • Stefanini L (2011) F-transform with parametric generalized fuzzy partitions. Fuzzy Sets Syst 180(1):98–120

  • Strauss O (2015) Non-additive interval-valued f-transform. Fuzzy Sets Syst 270:1–24

    Article  MathSciNet  Google Scholar 

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

  • Tomasiello S (2016) An alternative use of fuzzy transform with application to a class of delay differential equations. Int J Comput Math (In press). doi:10.1080/00207160.2016.1227436

  • Tomasiello S, Gaeta M, Loia V (2016) Quasi-consensus in second-order multi-agent systems with sampled data through fuzzy transform. J Uncertain Syst 10(4):243–250

    Google Scholar 

  • Troiano L, Kriplani P (2011) Supporting trading strategies by inverse fuzzy transform. Fuzzy Sets Syst 180:121–145

    Article  MathSciNet  MATH  Google Scholar 

  • Štěpnička M, Valášek R (2003) Fuzzy transforms for function with two variables. In: Proceedings of the 6th Czech–Japan seminar on data analysis and decision making under uncertainity, pp 100–107

  • Štěpnička M, Valášek R (2004) Fuzzy transforms and their application to wave equation. J Electr Eng 55(12):7–10

    MATH  Google Scholar 

  • Štěpnička M, Valášek R (2005) Numerical solution of partial differential equations with help of fuzzy transform. In: Proceedings of IEEE international conference on fuzzy systems, IEEE, pp 1104–1109

  • Wand MP, Jones M (1995) Kernel Smoothing. Chapman & Hall/CRC Monographs on Statistics & Applied Probability, London

  • Yaglom AM (1962) An introduction to the theory of stationary random functions. Revised English ed. Translated and edited by Richard A. Silverman. Englewood Cliffs, vol 13, Prentice-Hall, Inc., NJ, p 235

  • Zadeh LA (1965) Fuzzy sets. Inf Control 8:338–353

    Article  MATH  Google Scholar 

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Acknowledgements

This work was supported by the project LQ1602 IT4Innovations excellence in science. The additional support was provided by the Czech Science Foundation through the Project of No.16-09541S.

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Correspondence to Michal Holčapek.

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Communicated by F. Di Martino, V. Novák.

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Nguyen, L., Holčapek, M. & Novák, V. Multivariate fuzzy transform of complex-valued functions determined by monomial basis. Soft Comput 21, 3641–3658 (2017). https://doi.org/10.1007/s00500-017-2658-8

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