| dc.creator | Rayas-Sánchez, José E. | |
| dc.creator | Gutiérrez-Ayala, Vladimir | |
| dc.date | 2013-05-21T17:02:57Z | |
| dc.date | 2013-05-21T17:02:57Z | |
| dc.date | 2006-11 | |
| dc.date.accessioned | 2023-07-21T22:12:11Z | |
| dc.date.available | 2023-07-21T22:12:11Z | |
| dc.identifier | 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. | |
| dc.identifier | http://hdl.handle.net/11117/590 | |
| dc.identifier.uri | https://repositorioslatinoamericanos.uchile.cl/handle/2250/7761628 | |
| dc.description | 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. | |
| dc.description | ITESO, A.C. | |
| dc.description | Consejo Nacional de Ciencia y Tecnología | |
| dc.format | application/pdf | |
| dc.language | eng | |
| dc.publisher | International Conference on Electronic Design | |
| dc.relation | International Conference on Electronic Design (ICED);2006 | |
| dc.rights | http://quijote.biblio.iteso.mx/licencias/CC-BY-NC-ND-2.5-MX.pdf | |
| dc.subject | Neural Space Mapping (NSM) | |
| dc.subject | High-frequency Circuits Design | |
| dc.title | High-frequency circuit design using a neural space-mapping algorithm based on a two-layer perceptron with optimized nonlinearity | |
| dc.type | info:eu-repo/semantics/conferencePaper | |
