Premium
Doubly curved aperture antenna shape prediction with the use of network‐based predictors
Author(s) -
Punhani Amitesh,
Washington Gregory,
Theunissen Wilhelmus H.
Publication year - 2002
Publication title -
microwave and optical technology letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.304
H-Index - 76
eISSN - 1098-2760
pISSN - 0895-2477
DOI - 10.1002/mop.10263
Subject(s) - radiation pattern , artificial neural network , aperture (computer memory) , antenna (radio) , microwave , reflector (photography) , least squares function approximation , antenna aperture , antenna height considerations , engineering , optics , algorithm , computer science , mathematics , artificial intelligence , telecommunications , structural engineering , physics , statistics , light source , estimator
The main objective of this work is to predict the shape of an antenna subreflector that produces a desired radiation far‐field pattern by utilizing artificial intelligence and other methodologies. In this study the size of the radiation beam is kept constant while it is steered throughout the domain. Three different methodologies or constructs are used to develop this model: Neural networks, batch least squares, and recursive least squares. The accuracy of a method is measured by the sum‐squared error of the training examples. During training the variables inside of the constructs are varied, so that the predicted aperture shape matches the actual shape. The networks predicted the antenna reflector shape at an average accuracy of over 97%, the maximum being 99.78%. © 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 33: 156–163, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10263