doctoralThesis
Otimização de superfícies seletivas de frequência com elementos pré-fractais utilizando rede neural MLP e algoritmos de busca populacional
Fecha
2014-01-27Registro en:
SILVA, Marcelo Ribeiro da. Otimização de superfícies seletivas de frequência com
elementos pré-fractais utilizando rede neural MLP e
algoritmos de busca populacional. 2014. 102 f. Tese (Doutorado em Automação e Sistemas; Engenharia de Computação; Telecomunicações) - Universidade Federal do Rio Grande do Norte, Natal, 2014.
Autor
Silva, Marcelo Ribeiro da
Resumen
This thesis describes design methodologies for frequency selective surfaces (FSSs)
composed of periodic arrays of pre-fractals metallic patches on single-layer dielectrics (FR4,
RT/duroid). Shapes presented by Sierpinski island and T fractal geometries are exploited to
the simple design of efficient band-stop spatial filters with applications in the range of
microwaves. Initial results are discussed in terms of the electromagnetic effect resulting from
the variation of parameters such as, fractal iteration number (or fractal level), fractal iteration
factor, and periodicity of FSS, depending on the used pre-fractal element (Sierpinski island or
T fractal). The transmission properties of these proposed periodic arrays are investigated
through simulations performed by Ansoft DesignerTM and Ansoft HFSSTM commercial
softwares that run full-wave methods. To validate the employed methodology, FSS prototypes
are selected for fabrication and measurement. The obtained results point to interesting features
for FSS spatial filters: compactness, with high values of frequency compression factor; as
well as stable frequency responses at oblique incidence of plane waves. This thesis also
approaches, as it main focus, the application of an alternative electromagnetic (EM)
optimization technique for analysis and synthesis of FSSs with fractal motifs. In application
examples of this technique, Vicsek and Sierpinski pre-fractal elements are used in the optimal
design of FSS structures. Based on computational intelligence tools, the proposed technique
overcomes the high computational cost associated to the full-wave parametric analyzes. To
this end, fast and accurate multilayer perceptron (MLP) neural network models are developed
using different parameters as design input variables. These neural network models aim to
calculate the cost function in the iterations of population-based search algorithms. Continuous
genetic algorithm (GA), particle swarm optimization (PSO), and bees algorithm (BA) are
used for FSSs optimization with specific resonant frequency and bandwidth. The performance
of these algorithms is compared in terms of computational cost and numerical convergence.
Consistent results can be verified by the excellent agreement obtained between simulations
and measurements related to FSS prototypes built with a given fractal iteration