@misc{10481/88725,
year = {2022},
url = {https://hdl.handle.net/10481/88725},
abstract = {In this paper, a robust approach to improve the performance of a condition monitoring process in industrial plants by using Pythagorean membership grades is presented. The FCM algorithm is modified by using Pythagorean fuzzy sets, to obtain
a new variant of it called Pythagorean Fuzzy C-Means (PyFCM). In addition, a kernel version of PyFCM (KPyFCM) is obtained in order to achieve greater separability among classes, and reduce classification errors. The approach proposed is validated using
experimental datasets and the Tennessee Eastman (TE) process benchmark. The results are compared with the results obtained with other algorithms that use standard and non-standard membership grades. The highest performance obtained by the approach
proposed indicate its feasibility.},
organization = {TIN2017-86647-P from the Spanish Ministry of Economy and Competitivenes, including FEDER funds},
organization = {Fundacão Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro (FAPERJ)},
organization = {Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)},
organization = {Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) (Finance Code 001) provided by the project CAPES-PRINT Process No. 88881.311758/2018-01,},
organization = {Universidad Tecnológica de La Habana José Antonio Echeverría, Cuba},
publisher = {Anais da Academia Brasileira de Ciências},
title = {An approach to robust condition monitoring in industrial processes using pythagorean membership grades},
doi = {10.1590/0001-3765202220200662},
author = {Cruz Corona, Carlos Alberto and Rodríguez Ramos, Adrián and Bernal de Lázaro, José M. and Llanes Santiago, Orestes and Da Silva Neto, Antônio José},
}