TY - JOUR
T1 - ANFIS system for classification of brain signals
AU - Rubio, José De Jesús
AU - Cruz, David Ricardo
AU - Elias, Israel
AU - Ochoa, Genaro
AU - Balcazar, Ricardo
AU - Aguilar, Arturo
N1 - Publisher Copyright:
© 2019-IOS Press and the authors. All rights reserved.
PY - 2019
Y1 - 2019
N2 - Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational costs results.
AB - Recently, the Adaptive-Network-Based Fuzzy Inference System (ANFIS) is applied in many areas of knowledge, and there are multiple optimization algorithms for its learning. This work shows the design of a novel optimization algorithm for an ANFIS system that learns and classifies the behavior of brain signals between normal and abnormal. For this goal, different types of optimization algorithms for the learning of an ANFIS system are evaluated, such as the backpropagation, the mini-lots, and the Adam algorithm (adaptive moment estimation). As a result, utilizing the ANFIS with Adam and mini-lots provides the most accurate, fastest, and with least computational costs results.
KW - ANFIS system
KW - Adam algorithm
KW - classification of brain signals
KW - mini-lots
UR - http://www.scopus.com/inward/record.url?scp=85073710483&partnerID=8YFLogxK
U2 - 10.3233/JIFS-190207
DO - 10.3233/JIFS-190207
M3 - Artículo
SN - 1064-1246
VL - 37
SP - 4033
EP - 4041
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 3
ER -