General regression neural network approach to prediction of electric field level in the reverberation chamber
by Muhammet Hilmi Nisanci, Yavuz Cengiz, Ovunc Polat, Antonio Orlandi, Alistair Duffy
International Journal of Reasoning-based Intelligent Systems (IJRIS), Vol. 2, No. 3/4, 2010

Abstract: This study presents an approach for the prediction of electric field level which depends on the positions of stirrer and frequency in the mode stirred reverberation chamber. A general regression neural network (GRNN) is used for prediction process. In order to show the system performance, feature selective validation (FSV) technique is given. The simulation results show that the predicted values of electrical field have been obtained with high accuracy. Thus, this technique will facilitate estimation of electric field level in the reverberation chamber which is component of especially mode stirrer reverberation chamber test method.

Online publication date: Fri, 12-Nov-2010

The full text of this article is only available to individual subscribers or to users at subscribing institutions.

 
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.

Pay per view:
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 Reasoning-based Intelligent Systems (IJRIS):
Login with your Inderscience username and password:

Username:        Password:          Forgotten your 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