General regression neural network approach to prediction of electric field level in the reverberation chamber
Date
2010
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Journal Title
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ISSN
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Publisher
Inderscience
Type
Article
Peer reviewed
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.
Description
Keywords
Computing and mathematics, Computing Science, Applications and Software, Electronic Systems, Control and Artificial Intelligence, Science, engineering and technology
Citation
Nisanci, M.H., Cengiz, Y., Polat, O., Orlandi, A. and Duffy, A. (2010) General regression neural network approach to prediction of electric field level in the reverberation chamber. Int Journal of Reasoning-based Intelligent Systems, 2 (3/4) pp 168-175