Blucher Engineering Proceedings

Dezembro 2024, vol.11, num.2 - X Simpósio Internacional de Inovação e Tecnologia
Full Article - Open Access Idioma principal | Segundo principal

USE OF ARTIFICIAL NEURAL NETWORKS FOR MULTILAYER WAVEGUIDE ANALYSIS

USE OF ARTIFICIAL NEURAL NETWORKS FOR MULTILAYER WAVEGUIDE ANALYSIS

Silva Neto, Arlindo José da; Cerqueira, Roney das Mercês; Sisnando, Anderson Dourado

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" The need to optimize processes has led to the construction of methodscapable of performing tasks with more consistency and accuracy. The present workproposes the optimization of a multilayer waveguide through computational algorithms.Neural Networks were developed with different configurations and trained with differentmethods: Cross Validation and Standard Training. The architecture consists of 7 inputsparameters and one output, with 3 hidden layers. The neural network with the bestperformance presented a Mean Square Error of 1.070×10-11, regression 0.999996 and16.25 seconds of processing, highlighting the Tansig activation function, evaluated asthe best function among those used."

" The need to optimize processes has led to the construction of methodscapable of performing tasks with more consistency and accuracy. The present workproposes the optimization of a multilayer waveguide through computational algorithms.Neural Networks were developed with different configurations and trained with differentmethods: Cross Validation and Standard Training. The architecture consists of 7 inputsparameters and one output, with 3 hidden layers. The neural network with the bestperformance presented a Mean Square Error of 1.070×10-11, regression 0.999996 and16.25 seconds of processing, highlighting the Tansig activation function, evaluated asthe best function among those used."

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DOI: 10.5151/siintec2024-393211

Referências bibliográficas
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Como citar:

Silva Neto, Arlindo José da; Cerqueira, Roney das Mercês; Sisnando, Anderson Dourado; "USE OF ARTIFICIAL NEURAL NETWORKS FOR MULTILAYER WAVEGUIDE ANALYSIS", p-1204-1210. In: . São Paulo: Blucher, 2024.
ISSN 23577592, DOI 10.5151/siintec2024-393211

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