TY - JOUR
T1 - Error convergence analysis of the SUFIN and CSUFIN
AU - de Jesús Rubio, José
N1 - Publisher Copyright:
© 2018 Elsevier B.V.
PY - 2018/11
Y1 - 2018/11
N2 - Sequential update fuzzy inference network (SUFIN), and changed sequential update fuzzy inference network (CSUFIN) are two evolving intelligent algorithms utilized for the modelling in prognostic health management plants. In this research, error convergence of the SUFIN and CSUFIN is analyzed. SUFIN utilizes the extended Kalman filter, while CSUFIN uses the gradient descent technique. First, proposed algorithms are linearized to get their modelling dynamic equations. Second, Lyapunov strategy is utilized to ensure the error convergence of studied networks. Two examples show the performance of advised algorithms.
AB - Sequential update fuzzy inference network (SUFIN), and changed sequential update fuzzy inference network (CSUFIN) are two evolving intelligent algorithms utilized for the modelling in prognostic health management plants. In this research, error convergence of the SUFIN and CSUFIN is analyzed. SUFIN utilizes the extended Kalman filter, while CSUFIN uses the gradient descent technique. First, proposed algorithms are linearized to get their modelling dynamic equations. Second, Lyapunov strategy is utilized to ensure the error convergence of studied networks. Two examples show the performance of advised algorithms.
KW - CSUFIN
KW - Error convergence analysis
KW - PHM
KW - SUFIN
UR - http://www.scopus.com/inward/record.url?scp=85045186427&partnerID=8YFLogxK
U2 - 10.1016/j.asoc.2018.04.003
DO - 10.1016/j.asoc.2018.04.003
M3 - Artículo
SN - 1568-4946
VL - 72
SP - 587
EP - 595
JO - Applied Soft Computing Journal
JF - Applied Soft Computing Journal
ER -