Skip to main content
Log in

Complex fuzzy sets with applications in signals

  • Published:
Computational and Applied Mathematics Aims and scope Submit manuscript

Abstract

A complex fuzzy set is characterized by a membership function, whose range is not limited to [0, 1], but extended to the unit circle in the complex plane. In this paper, we introduce some new operations and laws of a complex fuzzy set such as disjunctive sum, simple difference, bounded difference, distributive law of union over intersection and intersection over union, equivalence formula, symmetrical difference formula, involution law, absorption law, and idempotent law. We introduce some basic results on complex fuzzy sets with respect to standard complex fuzzy intersection, union, and complement functions corresponding to the same functions for determining the phase term, and we give particular examples of these operations. We use complex fuzzy sets in signals and systems, because its behavior is similar to a Fourier transform in certain cases. Moreover, we develop a new algorithm using complex fuzzy sets for applications in signals and systems by which we identify a reference signal out of large number of signals detected by a digital receiver. We use the inverse discrete Fourier transform of a complex fuzzy set for incoming signals and a reference signal. Thus, a method for measuring the exact values of two signals is provided by which we can identity the reference signal.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Alcantud JCR, Calle RDA (2017) The problem of collective identity in a fuzzy environment. Fuzzy Sets Syst 315:57–75

    Article  MathSciNet  Google Scholar 

  • Ali M, Smarandache F (2017) Complex neutrosophic set. Neural Comput Appl 28:1817–1834

    Article  Google Scholar 

  • Alkouri A, Salleh A (2012) Complex intuitionistic fuzzy sets. In: International conference on fundamental and applied sciences, AIP conference proceedings, vol 1482, pp 464–470

  • Dick S (2005) Toward complex fuzzy logic. IEEE Trans Fuzzy Syst 13:405–414

    Article  Google Scholar 

  • Dubois D, Prade H (2000) Fundamentals of fuzzy sets. Khuwer Academic Publisher, Boston

    Book  Google Scholar 

  • El Allaoui A, Melliani S, Chadli LS (2017) Representation of complex grades of membership and non-membership for a complex intuitionistic fuzzy sets. Notes Intuit Fuzzy Sets 23(5):51–60

    MATH  Google Scholar 

  • Garrido A (2007) Fuzzy mathematical analysis, Conference Paper, September

  • Hu B, Bi L, Dai S (2017) The orthogonality between complex fuzzy sets and its application to signal detection. Symmetry 9(9):175

    Article  Google Scholar 

  • Li HX, Yen VC (1995) Fuzzy sets and fuzzy decision making. CRC Press, London

    MATH  Google Scholar 

  • Naz S, Akram M (2019) Novel decision-making approach based on hesitant fuzzy sets and graph theory. Comput Appl Math 38(1):7

    Article  MathSciNet  Google Scholar 

  • Ngan TT, Lan LTH, Ali M, Tamir D, Son LH, Tuan TM, Rishe N, Kandel A (2018) Logic connectives of complex fuzzy sets. Roman J Inf Sci Technol 21(4):344–357

    Google Scholar 

  • Nguyen HT, Walker EA (2006) Fuzzy logic. CRC Press, New York

    Google Scholar 

  • Nguyen HT, Kreinovich V, Shekhter V (1998) On the possibility of using complex values in fuzzy logic for representing inconsistencies. Int J Intell Syst 13(8):683–714

    Article  Google Scholar 

  • Nisren GS, Hafeed A, Salleh AR (2017) Complex fuzzy soft expert sets. AIP Conf Proc 1830(070020):1–8

    MATH  Google Scholar 

  • Pedrycz W, Gomide F (1998) An introduction to fuzzy sets. MIT Press, Cambridge

    Book  Google Scholar 

  • Peng XD, Dai J (2018a) A bibliometric analysis of neutrosophic set: two decades review from 1998 to 2017. Artif Intell Rev. https://doi.org/10.1007/s10462-018-9652-0

    Article  Google Scholar 

  • Peng XD, Dai J (2018b) Approaches to single-valued neutrosophic MADM based on MABAC, TOPSIS and new similarity measure with score function. Neural Comput Appl 29(10):939–954

    Article  Google Scholar 

  • Peng XD, Garg H (2018) Algorithms for interval-valued fuzzy soft sets in emergency decision making based on WDBA and CODAS with new information measure. Comput Ind Eng 119:439–452

    Article  Google Scholar 

  • Peng XD, Selvachandran G (2017) Pythagorean fuzzy set: state of the art and future directions. Artif Intell Rev 52:1873–1927

    Article  Google Scholar 

  • Peng XD, Yuan H, Yang Y (2017) Pythagorean fuzzy information measures and their applications. Int J Intell Syst 32(10):991–1029

    Article  Google Scholar 

  • Peng XD, Dai J, Garg H (2018) Exponential operation and aggregation operator for q-rung orthopair fuzzy set and their decision-making method with a new score function. Int J Intell Syst 33(11):2255–2282

    Article  Google Scholar 

  • Poodeh OY (2017) Applications of complex fuzzy sets in time-series prediction, Ph.D Thesis, University of Alberta

  • Ramot D, Milo R, Friedman M, Kandel A (2002) Complex fuzzy sets. IEEE Trans Fuzzy Syst 10(2):171–186

    Article  Google Scholar 

  • Ramot D, Friedman M, Langholz G, Kandel A (2003) Complex fuzzy logic. IEEE Trans Fuzzy Syst 11(4):450–461

    Article  Google Scholar 

  • Selesnick IW, Schuller G (2001) The discrete Fourier transform, 2nd chapter. In: Rao KR, Yip PC (eds) The transform and data compression handbook. CRC Press, Boca Raton

    Google Scholar 

  • Singh PK (2017) Complex vague set based concept lattice. Chaos Solitons Fractals 96:145–153

    Article  Google Scholar 

  • Tamir DE, Jin L, Kandel A (2011) A new interpretation of complex membership grade. Int J Intell Syst 26:285–312

    Article  Google Scholar 

  • Yazdanbakhsh O, Dick S (2018) A systematic review of complex fuzzy sets and logic. Fuzzy Sets Syst 338:1–22

    Article  MathSciNet  Google Scholar 

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

    Article  Google Scholar 

  • Zhang G, Dillon ST, Cai YK, Ma J, Lu J (2009) Operation properties and \(\delta \)-equalities of complex fuzzy sets. Int J Approx Reason 50:1227–1249

    Google Scholar 

  • Zhou Y, Cao W, Liu L, Agaian S, Chen CLP (2015) Fast Fourier transform using matrix decomposition. Inf Sci 291:172–183

    Article  MathSciNet  Google Scholar 

Download references

Acknowledgements

The authors are thankful to associate editor Marcos Eduardo Valle and anonymous referees for their valuable comments on our manuscript. This research is partially supported by NNSFC (61866011; 11561023). The third author is thankful to Higher Education Commission of Pakistan for the financial support.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jianming Zhan.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interests.

Additional information

Communicated by Marcos Eduardo Valle.

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Ma, X., Zhan, J., Khan, M. et al. Complex fuzzy sets with applications in signals. Comp. Appl. Math. 38, 150 (2019). https://doi.org/10.1007/s40314-019-0925-2

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s40314-019-0925-2

Keywords

Mathematics Subject Classification

Navigation