A faster algorithm for identifying signals using complex fuzzy sets

In this paper, we established some new operations and formulas of set theory for complex fuzzy sets (CFSs). We introduced the basic results of CFSs with their examples using union, intersection, complement, dot product, complex fuzzy probalistic sum, complex fuzzy bold sum, complex fuzzy bold sum over associative law of union, etc. Moreover, we introduced an algorithm to identify a reference signal out of large number of signal having bigger NSamples\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\left( {Samples} \right)$$\end{document} received by a digital receiver. Thus, a new model is introduced for measuring the values of the signals in a faster way using CFSs.