@article{MAKHILLJEAS2021161019559,
    title = {Inverted Pendulum Control using NARMA-L2 with Resilient Backpropagation and Levenberg
Marquardt Backpropagation Training Algorithm},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {16},
    number = {10},
    pages = {324-330},
    year = {2021},
    issn = {1816-949x},
    doi = {jeasci.2021.324.330},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2021.324.330},
    author = {Mustefa,Nuriye and},
    keywords = {resilient backpropagation,NARMA-L2,Inverted pendulum,Levenberg Marquardt backpropagation},
    abstract = {In this study, the performance of inverted
pendulum has been Investigated using neural network
control theory. The proposed controllers used in this study
are NARMA-L2 with resilient backpropagation and
Levenberg Marquardt backpropagation algorithm
controllers. The mathematical model of Inverted
Pendulum on a Cart driving mechanism have been done
successfully. Comparison of an inverted pendulum with
NARMA-L2 with resilient backpropagation and
Levenberg Marquardt backpropagation algorithm
controllers for a control target deviation of an angle from
vertical of the inverted pendulum using two input signals
(step and random). The simulation result shows that the
inverted pendulum with NARMA-L2 with resilient
backpropagation controller to have a small rise time,
settling time and percentage overshoot in the step
response and having a good response in the random
response too. Finally, the inverted pendulum with with
NARMA-L2 with resilient backpropagation controller
shows the best performance in the overall simulation
result.}
    }