TY - JOUR
T1 - A New Design of Mamdani Complex Fuzzy Inference System for Multiattribute Decision Making Problems
AU - Selvachandran, Ganeshsree
AU - Quek, Shio Gai
AU - Lan, Luong Thi Hong
AU - Son, Le Hoang
AU - Giang, Nguyen Long
AU - Ding, Weiping
AU - Abdel-Basset, Mohamed
AU - De Albuquerque, Victor Hugo C.
N1 - Funding Information:
Manuscript received July 27, 2019; revised November 8, 2019; accepted December 13, 2019. Date of publication December 20, 2019; date of current version March 31, 2021. The work of G. Selvachandran and Q. Shio was supported by the Ministry of Education, Malaysia, under Grant FRGS/1/2017/STG06/UCSI/03/1. The work of L. T. H. Lan, L. H. Son, and N. L. Giang was supported by the Vietnam Academy of Science and Technology under Research Project VAST 01.05/19-20. The work of W. Ding was supported in part by the National Natural Science Foundation of China under Grant 61976120, in part by the Natural Science Foundation of Jiangsu Province under Grant BK20191445, in part by the Six Talent Peaks Project of Jiangsu Province under Grant XYDXXJS-048, and in part by the Qing Lan Project of Jiangsu Province. (Corresponding authors: Weiping Ding; Mohamed Abdel-Basset.) G. Selvachandran is with the Department of Actuarial Science and Applied Statistics, Faculty of Business & Information Science, UCSI University, Jalan Menara Gading, Cheras 56000, Malaysia (e-mail: [email protected]).
Publisher Copyright:
© 1993-2012 IEEE.
PY - 2021/4
Y1 - 2021/4
N2 - This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations.
AB - This article proposes the Mamdani complex fuzzy inference system (Mamdani CFIS) to improve performance of the classical FIS and complex FIS. The applicability of the proposed CFIS is demonstrated by applying it to six commonly available datasets from UCI Machine Learning under the comparison with Mamdani FIS and the Adaptive Neuro Complex Fuzzy Inference System (ANCFIS). It is successfully proven that the proposed Mamdani CFIS is computationally less expensive and presents a more efficient method to handle time-series data and time-periodic phenomena, among all the fuzzy IS found thus far in the literature. Furthermore, the novelty of CFIS mainly lies in its implementation of the complex number throughout the entire procedures of computation. This gives much greater flexibility of implementing unexpected, nonlinear fluctuations.
KW - Complex fuzzy inference system (CFIS)
KW - complex fuzzy logic (CFL)
KW - decision making
KW - Mamdani fuzzy inference system (FIS)
UR - http://www.scopus.com/inward/record.url?scp=85103881370&partnerID=8YFLogxK
U2 - 10.1109/TFUZZ.2019.2961350
DO - 10.1109/TFUZZ.2019.2961350
M3 - Article
AN - SCOPUS:85103881370
SN - 1063-6706
VL - 29
SP - 716
EP - 730
JO - IEEE Transactions on Fuzzy Systems
JF - IEEE Transactions on Fuzzy Systems
IS - 4
M1 - 8937744
ER -