A New Approach of Multi-Parameter UWB Antenna Modeling Based on Knowledge-Based Artificial Neural Network
A New Approach of Multi-Parameter UWB Antenna Modeling Based on Knowledge-Based Artificial Neural Network
- Author(s): M. Ojaroudi ; S. Bila ; F. Torrès
- DOI: 10.1049/cp.2018.0497
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- Author(s): M. Ojaroudi ; S. Bila ; F. Torrès Source: 12th European Conference on Antennas and Propagation (EuCAP 2018), 2018 page (5 pp.)
- Conference: 12th European Conference on Antennas and Propagation (EuCAP 2018)
- DOI: 10.1049/cp.2018.0497
- ISBN: 978-1-78561-816-1
- Location: London, UK
- Conference date: 9-13 April 2018
- Format: PDF
In this paper, we present a new approach of high accuracy multi-parameter modeling using artificial neural network (ANN) for an ultra-wideband (UWB) printed monopole antenna. In order to demonstrate the usefulness of the neural network for high-accuracy multi-parameter modeling, the electrical parameters of the antenna such as return loss and maximum gain have been predicted using some physical parameters such as widths and lengths of the slots or sleeves. The advantage conferred by the proposed method is that fast and intelligent modeling method is available at multi-parameter structures in contrast with conventional blind parametric sweep simulation, and so a lower memory and higher accuracy of the behavioral model is achieved. It can also be advantageous when the user is looking for system level, as the increased components amounts can help as a surrogate model. Simulated and predicted via model results are presented to validate the usefulness of the proposed method.
Inspec keywords: ultra wideband antennas; monopole antennas; microstrip antennas; neural nets
Subjects: Single antennas; Neural computing techniques
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