proposed structure reduces not only the number of semiconductor switches, but also the size of the phase shifter. ## CONCLUSION A shunt-connected single-switch structure has been presented for phase-shifting applications. Starting from analytic results, design criteria have been given. The perfor
Tunnel-based artificial neural network technique to calculate the resonant frequency of a thick-substrate microstrip antenna
β Scribed by Shyam S. Pattnaik; Dhruba C. Panda; S. Devi
- Publisher
- John Wiley and Sons
- Year
- 2002
- Tongue
- English
- Weight
- 250 KB
- Volume
- 34
- Category
- Article
- ISSN
- 0895-2477
No coin nor oath required. For personal study only.
β¦ Synopsis
Abstract
The mathematical formulation of empirically developed formulas for the calculation of the resonant frequency of a thickβsubstrate (h β₯ 0.08151Ξ»~0~) microstrip antenna has been analyzed. With the use of tunnelβbased artificial neural networks (ANNs), the resonant frequency of antennas with h satisfying the thickβsubstrate condition are calculated and compared with the existing experimental results and also with the simulation results obtained with the use of an IE3D software package. The artificial neural network results are in very good agreement with the experimental results. Β© 2002 Wiley Periodicals, Inc. Microwave Opt Technol Lett 34: 460β462, 2002; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.10495
π SIMILAR VOLUMES
Existing methods to compute the bandwidth of probe-fed rectangular microstrip antenna elements are inaccurate for the range of substrate thicknesses considered for this research. When 0.0815h0 5 h (A0 is the free-space wacrelength and h is the substrate thickness) the effect of surface wacies is pre
## Abstract Both genetic algorithms (GAs) and artificial neural networks (ANNs) have been used in the field of computational electromagnetics as the most powerful optimizing tools. In this paper, a simple and efficient method is presented to handle the problem of competing convention while training
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