TY - GEN
T1 - A Novel Bivariate Elliptic Fuzzy Membership Function
T2 - Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
AU - Basaran, Alparslan Abdurrahman
AU - Basaran, Murat Alper
AU - Demir, Mehmet Ozer
N1 - Publisher Copyright:
© 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2023
Y1 - 2023
N2 - In this paper, the data set generated by Bike Sharing System (BSS) has been modeled by a proposed method called fuzzy bivariate elliptic membership function that generates membership values between an independent variable and dependent variable whose functional form follows an ellipse since all numerical variables following a cyclic pattern such as season, month, hour, weather situation and so on. Besides, each membership value corresponding to each independent and dependent variable is used to find an aggregate outcome of a dependent variable based on a new decision-making tool. Therefore, how both weather and time combinations have an impact on the dependent variable could be derived. Since there is no built-in membership function available, the data set is used to construct a data-driven elliptic fuzzy membership function. Thus, the Chebyshev inequality based on the correlated variables is used to determine both the a and b parameters of the elliptic function representing 95% of the whole dataset.
AB - In this paper, the data set generated by Bike Sharing System (BSS) has been modeled by a proposed method called fuzzy bivariate elliptic membership function that generates membership values between an independent variable and dependent variable whose functional form follows an ellipse since all numerical variables following a cyclic pattern such as season, month, hour, weather situation and so on. Besides, each membership value corresponding to each independent and dependent variable is used to find an aggregate outcome of a dependent variable based on a new decision-making tool. Therefore, how both weather and time combinations have an impact on the dependent variable could be derived. Since there is no built-in membership function available, the data set is used to construct a data-driven elliptic fuzzy membership function. Thus, the Chebyshev inequality based on the correlated variables is used to determine both the a and b parameters of the elliptic function representing 95% of the whole dataset.
KW - Bike-sharing
KW - Chebyshev’s inequality
KW - Decision making
KW - Elliptic fuzzy membership function
UR - https://www.scopus.com/pages/publications/85171998973
U2 - 10.1007/978-3-031-39774-5_10
DO - 10.1007/978-3-031-39774-5_10
M3 - Conference contribution
AN - SCOPUS:85171998973
SN - 9783031397738
T3 - Lecture Notes in Networks and Systems
SP - 77
EP - 84
BT - Intelligent and Fuzzy Systems - Intelligence and Sustainable Future Proceedings of the INFUS 2023 Conference
A2 - Kahraman, Cengiz
A2 - Sari, Irem Ucal
A2 - Oztaysi, Basar
A2 - Cevik Onar, Sezi
A2 - Cebi, Selcuk
A2 - Tolga, A. Çağrı
PB - Springer Science and Business Media Deutschland GmbH
Y2 - 22 August 2023 through 24 August 2023
ER -