## 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
A genetic algorithm approach to the design of ultra-thin electromagnetic bandgap absorbers
✍ Scribed by D. J. Kern; D. H. Werner
- Publisher
- John Wiley and Sons
- Year
- 2003
- Tongue
- English
- Weight
- 226 KB
- Volume
- 38
- Category
- Article
- ISSN
- 0895-2477
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✦ Synopsis
Abstract
A design methodology is presented for utilizing electromagnetic bandgap metamaterials, also known as artificial magnetic conductors, to realize ultra‐thin absorbers. One approach that has recently been proposed is to place a resistive sheet in close proximity to a frequency‐selective surface acting as an artificial magnetic conductor. However, we demonstrate in this paper that incorporating the loss directly into the frequency selective‐surface can eliminate the additional resistive sheet, thereby further reducing the overall thickness of the absorber. The geometrical structure and corresponding resistance of this lossy frequency‐selective surface is optimized by using a genetic algorithm to achieve the thinnest possible absorber. Two examples of genetically engineered electromagnetic bandgap metamaterial absorbers are presented and discussed. © 2003 Wiley Periodicals, Inc. Microwave Opt Technol Lett 38: 61–64, 2003
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