This paper considers the trouble of the usage of approximate strategies
for realizing the neural controllers for
nonlinear SISO systems. In this paper, we introduce the nonlinear
autoregressive moving average (NARMA-L2)
model which might be approximations to the NARMA model. The nonlinear
autoregressive moving average
(NARMA-L2) model is an precise illustration of the input–output behavior
of finite-dimensional nonlinear discrete
time dynamical systems in a neighborhood of the equilibrium state.
However, it isn't always handy for purposes
of neural networks due to its nonlinear dependence on the manipulate
input. In this paper, nerves system based
arm position sensor device is used to degree the precise arm function
for nerve patients the use of the proposed
systems. In this paper, neural network controller is designed with
NARMA-L2 model, neural network controller
is designed with NARMA-L2 model system identification based predictive
controller and neural network
controller is designed with NARMA-L2 model based model reference adaptive
control system. Hence, quite
regularly, approximate techniques are used for figuring out the neural
controllers to conquer computational
complexity. Comparison were made among the neural network controller
with NARMA-L2 model, neural network
controller with NARMA-L2 model system identification based predictive
controller and neural network controller
with NARMA-L2 model reference based adaptive control for the preferred
input arm function (step, sine wave
and random signals). The comparative simulation result shows the effectiveness
of the system with a neural
network controller with NARMA-L2 model based model reference adaptive
control system.
:C\:\\Users\\user\\Desktop\\NEW JOURNAL FOR ORCID\\Nonlinear Autoregressive Moving Average-L2 Model Based Adaptive Control of Nonlinear Arm Nerve Simulator System.pdf:PDF
%0 Journal Article
%1 MustefaJibril2020
%A Mustefa Jibril, Dr.Prashanth Alluvada
%D 2020
%J Innovative Systems Design and Engineering
%K Model Nonlinear Predictive adaptive autoregressive average, control, controller moving network, neural reference
%N 2
%P 6-14
%R 10.7176/ISDE/11-2-02
%T Nonlinear Autoregressive Moving Average-L2
Model Based Adaptive Control of Nonlinear Arm Nerve
Simulator System
%V 11
%X This paper considers the trouble of the usage of approximate strategies
for realizing the neural controllers for
nonlinear SISO systems. In this paper, we introduce the nonlinear
autoregressive moving average (NARMA-L2)
model which might be approximations to the NARMA model. The nonlinear
autoregressive moving average
(NARMA-L2) model is an precise illustration of the input–output behavior
of finite-dimensional nonlinear discrete
time dynamical systems in a neighborhood of the equilibrium state.
However, it isn't always handy for purposes
of neural networks due to its nonlinear dependence on the manipulate
input. In this paper, nerves system based
arm position sensor device is used to degree the precise arm function
for nerve patients the use of the proposed
systems. In this paper, neural network controller is designed with
NARMA-L2 model, neural network controller
is designed with NARMA-L2 model system identification based predictive
controller and neural network
controller is designed with NARMA-L2 model based model reference adaptive
control system. Hence, quite
regularly, approximate techniques are used for figuring out the neural
controllers to conquer computational
complexity. Comparison were made among the neural network controller
with NARMA-L2 model, neural network
controller with NARMA-L2 model system identification based predictive
controller and neural network controller
with NARMA-L2 model reference based adaptive control for the preferred
input arm function (step, sine wave
and random signals). The comparative simulation result shows the effectiveness
of the system with a neural
network controller with NARMA-L2 model based model reference adaptive
control system.
@article{MustefaJibril2020,
abstract = {This paper considers the trouble of the usage of approximate strategies
for realizing the neural controllers for
nonlinear SISO systems. In this paper, we introduce the nonlinear
autoregressive moving average (NARMA-L2)
model which might be approximations to the NARMA model. The nonlinear
autoregressive moving average
(NARMA-L2) model is an precise illustration of the input–output behavior
of finite-dimensional nonlinear discrete
time dynamical systems in a neighborhood of the equilibrium state.
However, it isn't always handy for purposes
of neural networks due to its nonlinear dependence on the manipulate
input. In this paper, nerves system based
arm position sensor device is used to degree the precise arm function
for nerve patients the use of the proposed
systems. In this paper, neural network controller is designed with
NARMA-L2 model, neural network controller
is designed with NARMA-L2 model system identification based predictive
controller and neural network
controller is designed with NARMA-L2 model based model reference adaptive
control system. Hence, quite
regularly, approximate techniques are used for figuring out the neural
controllers to conquer computational
complexity. Comparison were made among the neural network controller
with NARMA-L2 model, neural network
controller with NARMA-L2 model system identification based predictive
controller and neural network controller
with NARMA-L2 model reference based adaptive control for the preferred
input arm function (step, sine wave
and random signals). The comparative simulation result shows the effectiveness
of the system with a neural
network controller with NARMA-L2 model based model reference adaptive
control system.},
added-at = {2020-09-01T13:57:51.000+0200},
author = {Mustefa Jibril, Dr.Prashanth Alluvada},
biburl = {https://www.bibsonomy.org/bibtex/2d3a876324114aaac668ef859bf49ec50/mustefa1981},
doi = {10.7176/ISDE/11-2-02},
file = {:C\:\\Users\\user\\Desktop\\NEW JOURNAL FOR ORCID\\Nonlinear Autoregressive Moving Average-L2 Model Based Adaptive Control of Nonlinear Arm Nerve Simulator System.pdf:PDF},
interhash = {7a68e088364401f509716b294b9f97ca},
intrahash = {d3a876324114aaac668ef859bf49ec50},
journal = {Innovative Systems Design and Engineering},
keywords = {Model Nonlinear Predictive adaptive autoregressive average, control, controller moving network, neural reference},
month = {March},
number = 2,
owner = {user},
pages = {6-14},
review = {peer reviewed},
timestamp = {2020-09-01T13:58:22.000+0200},
title = {Nonlinear Autoregressive Moving Average-L2
Model Based Adaptive Control of Nonlinear Arm Nerve
Simulator System},
volume = 11,
year = 2020
}