Analysis of signals with inexact data by using interval-valued functions
Levent, H; Yilmaz, Y; MANN RJ, 1965, J MATH ANAL APPL, V12, P1, DOI 10.1016/0022-247X(65)90049-1; Bozkurt H, 2016, COGENT MATH, V3, DOI 10.1080/23311835.2016.1194801; Crouzet JF, 2011, FUZZY SET SYST, V183, P92, DOI 10.1016/j.fss.2011.02.019; Graba F, 2016, FUZZY SET SYST, V288, P26, DOI 10.1016/j.fss.2015.08.020; Jaulin L., 2001, APPL INTERVAL ANAL E; Kotonaj E., 2014, INT J MATH TRENDS TE, V12, P63, DOI [10.14445/22315373/IJMTT-V12P510, DOI 10.14445/22315373/IJMTT-V12P510]; Kulisch, 1969, JB BERBLICKE MATH; Levent H, 2018, CARPATHIAN MATH PUBL, V10, P143, DOI 10.15330/cmp.10.1.143-164; Levent H., 2017, J BIOSTAT BIOMETRIC, V2, P101; Moore RE, 2009, INTRO INTERVAL ANAL
Tarih:
2022
Özet:
Mathematically, a signal is a function of an independent variable t and it contains information about the behavior of the physical quantity. In real life, sometimes a signal value in a time t may not be known exactly. This paper presents a new mathematical method for processing of such a non-deterministic signal by using interval-valued functions which is called as its model interval signal. If the properties of a signal are completely unknown then we cannot perform the processing of these signals such as determining the autocorrelation function of the non-deterministic signal. Especially, in this work, we give an application to estimate the autocorrelation function of a signal with inexact data. For this purpose we use some new mathematical methods so called quasilinear functional analysis. Our studies give approximative result, although there are no definite results for such signals. We think that it's better than not having any information.
C1 [Levent, Halise; Yilmaz, Yilmaz] Inonu Univ, Dept Math, TR-44280 Malatya, Turkey.
C3 Inonu University
Tüm öğe kaydını göster
Bu öğenin dosyaları:
|
Bu öğe ile ilişkili dosya yok.
|
Bu öğe aşağıdaki koleksiyon(lar)da görünmektedir.