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Non‐linear system identification and control based on neural and self‐tuning control
Author(s) -
Abdulaziz A.,
Farsi M.
Publication year - 1993
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.4480070407
Subject(s) - control theory (sociology) , feed forward , artificial neural network , identification (biology) , feedforward neural network , transfer function , computer science , parametric statistics , linearity , system identification , linear model , least squares function approximation , linear system , control (management) , mathematics , control engineering , engineering , artificial intelligence , statistics , machine learning , electronic engineering , data modeling , mathematical analysis , botany , electrical engineering , database , estimator , biology
This paper deals with identification and control of non‐linear systems that contain linear parameters and a non‐linear quasi‐input function of the real input signal. Least squares have been used to estimate the linear parametric part, while a neural feedforward net is employed to estimate the remaining non‐linearity. Performance considerations are illustrated by hybrid algorithm simulations.