RT Journal Article
T1 Fuzzy model identification and self learning with smooth compositions
A1 Sadjadi, Ebrahim
A1 García Herrero, Jesús
A1 Molina López, José Manuel
A1 Borzabadi, Akbar Hashemi
A1 Abchouyeh, Monireh Asadi
AB This Paper Develops A Smooth Model Identification And Self-Learning Strategy For Dynamic Systems Taking Into Account Possible Parameter Variations And Uncertainties. We Have Tried To Solve The Problem Such That The Model Follows The Changes And Variations In The System On A Continuous And Smooth Surface. Running The Model To Adaptively Gain The Optimum Values Of The Parameters On A Smooth Surface Would Facilitate Further Improvements In The Application Of Other Derivative Based Optimization Control Algorithms Such As Mpc Or Robust Control Algorithms To Achieve A Combined Modeling-Control Scheme. Compared To The Earlier Works On The Smooth Fuzzy Modeling Structures, We Could Reach A Desired Trade-Off Between The Model Optimality And The Computational Load. The Proposed Method Has Been Evaluated On A Test Problem As Well As The Non-Linear Dynamic Of A Chemical Process.
PB Springer
SN 1562-2479
YR 2019
FD 2019-10-03
LK https://hdl.handle.net/10016/33539
UL https://hdl.handle.net/10016/33539
LA eng
NO This publication was supported in part by project MINECO, TEC2017-88048-C2-2-R
DS e-Archivo
RD 17 jul. 2024