Neural Networks for Synthesis and Optimization of Antenna Arrays
Alternative metrics PlumXhttp://hdl.handle.net/11012/57272
MetadataShow full item record
This paper describes a usual application of back-propagation neural networks for synthesis and optimization of antenna array. The neural network is able to model and to optimize the antennas arrays, by acting on radioelectric or geometric parameters and by taking into account predetermined general criteria. The neural network allows not only establishing important analytical equations for the optimization step, but also a great flexibility between the system parameters in input and output. This step of optimization becomes then possible due to the explicit relation given by the neural network. According to different formulations of the synthesis problem such as acting on the feed law (amplitude and/or phase) and/or space position of the radiating sources, results on antennas arrays synthesis and optimization by neural networks are presented and discussed. However ANN is able to generate very fast the results of synthesis comparing to other approaches.
Document typePeer reviewed
Document versionFinal PDF
SourceRadioengineering. 2007, vol. 16, č. 1, s. 23-30. ISSN 1210-2512
- 2007/1