High-frequency circuit design using a neural space-mapping algorithm based on a two-layer perceptron with optimized nonlinearity
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Fecha
2006-11
Autores
Rayas-Sánchez, José E.
Gutiérrez-Ayala, Vladimir
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Editor
International Conference on Electronic Design
Resumen
Descripción
In this work we present an improved version of the
Neural Space-Mapping algorithm with regulated
nonlinearity. The new version uses a nonlinear two-layer
perceptron (2LP), instead of a three layer perceptron
(3LP), to train the space-mapping (SM)-based
neuromodel. The 2LP mapping nonlinearity is
automatically regulated with classical optimization
algorithms. Additionally, the new algorithm uses a
different optimization method to train the SM-based
neuromodel. With these three main improvements we
obtain a more efficient and faster algorithm. In order to
verify the algorithm performance, we design a stopband
microstrip filter with quarter-wave resonant opens stubs,
and a microstrip notch filter with mitered bends. Both
circuits use a full-wave electromagnetic simulator.
Palabras clave
Neural Space Mapping (NSM), High-frequency Circuits Design
Citación
V. Gutiérrez-Ayala and J. E. Rayas-Sánchez, “High-frequency circuit design using a neural space-mapping algorithm based on a two-layer perceptron with optimized nonlinearity,” in Int. Conf. on Electronic Design Proc. (ICED 2006), Veracruz, Mexico, Nov. 2006, pp. 90-95.