TY - JOUR
T1 - Neural network updating via argument Kalman filter for modeling of Takagi-Sugeno fuzzy models
AU - Rubio, José De Jesús
AU - Lughofer, Edwin
AU - Meda-Campaña, Jesús A.
AU - Páramo, Luis Alberto
AU - Novoa, Juan Francisco
AU - Pacheco, Jaime
N1 - Publisher Copyright:
© 2018 - IOS Press and the authors. All rights reserved.
PY - 2018
Y1 - 2018
N2 - In this article, an argument Kalman filter is exposed for the fast updating of a neural network. The argument Kalman filter is developed based on the extended Kalman filter, but the recommended scheme has the next two advantages: first, it has less computational complexity because it only employs the Jacobian argument instead of the full Jacobian, second, its gain is ensured to be uniformly stable based on the Lyapunov approach. The commented scheme is applied for the modeling of two Takagi-Sugeno fuzzy models.
AB - In this article, an argument Kalman filter is exposed for the fast updating of a neural network. The argument Kalman filter is developed based on the extended Kalman filter, but the recommended scheme has the next two advantages: first, it has less computational complexity because it only employs the Jacobian argument instead of the full Jacobian, second, its gain is ensured to be uniformly stable based on the Lyapunov approach. The commented scheme is applied for the modeling of two Takagi-Sugeno fuzzy models.
KW - Argument Kalman filter
KW - fuzzy models
KW - modeling
UR - http://www.scopus.com/inward/record.url?scp=85053298741&partnerID=8YFLogxK
U2 - 10.3233/JIFS-18425
DO - 10.3233/JIFS-18425
M3 - Artículo
SN - 1064-1246
VL - 35
SP - 2585
EP - 2596
JO - Journal of Intelligent and Fuzzy Systems
JF - Journal of Intelligent and Fuzzy Systems
IS - 2
ER -