Disturbance influence on neuronal system modelling Online publication date: Tue, 14-Feb-2017
by Ines Mahmoud; Ayachi Errachdi; Mohamed Benrejeb
International Journal of Computer Applications in Technology (IJCAT), Vol. 55, No. 1, 2017
Abstract: This paper focuses on the neural modelling of a system in the presence of disturbances. It proposes an offline and online identification of the inputs of a disturbed mobile robot using artificial neural networks. When meeting strong disturbances, the deviations between the predicted signal-based model and the real signal of the system may become large since usually the disturbances are not measurable and not included in the predictive model. The used disturbance, in this paper, is a random signal added to the output signal. The results of simulation show that the use of the neural networks in the identification of the inputs of the disturbed robot model is very interesting since it enables to guarantee the time competition and the quality of the modelling. The effectiveness of the proposed algorithm applied to the modelling of behaviour of the CHAR robot is verified by simulation experiments with two strategies, online and offline modelling of robot CHAR inputs. Simulation results demonstrate the effectiveness of the proposed control method.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Computer Applications in Technology (IJCAT):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook