TY  - JOUR
T1  - Adaptive Control using Nonlinear Autoregressive-Moving Average-L2 Model for Realizing
Neural Controller for Unknown Finite Dimensional Nonlinear Discrete Time Dynamical
Systems
AU - Jibril, Mustefa AU - Tadese, Messay AU - Hassen, Nuriye 
JO  - Journal of Engineering and Applied Sciences
VL  - 16
IS  - 3
SP  - 130
EP  - 137
PY  - 2021
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2021.130.137
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2021.130.137
KW  - NARMA-L2
KW  -NARMA-L1
KW  -neural network
AB  - This study considers the problem of using
approximate way for realizing the neural supervisor for
nonlinear multivariable systems. The Nonlinear
Autoregressive-Moving Average (NARMA) model is an
exact transformation of the input-output behavior of
finite-dimensional nonlinear discrete time dynamical
organization in a hoodlum of the equilibrium state.
However, it is not convenient for intention of adaptive
control using neural networks due to its nonlinear
dependence on the control input. Hence, quite often,
approximate technique are used for realizing the neural
supervisor to overcome computational complexity. In this
study, we introduce two classes of ideal which are
approximations to the NARMA model and which are
linear in the control input, namely NARMA-L1 and
NARMA-L2. The latter fact substantially simplifies both
the theoretical breakdown as well as the practical request
of the controller. Extensive imitation studies have shown
that the neural controller designed using the proposed
approximate models perform very well and in dozens
situation even better than an approximate controller
designed using the exact NARMA Model. In view of their
mathematical tractability as well as their fate in
simulation studies, a matter is made in this study that such
approximate input-output paragon warrants a detailed
study in their own right.
ER  -