RF-LNA circuit synthesis using an array of artificial neural networks with constrained inputs

Dumesnil, Etienne, Nabki, Frederic et Boukadoum, Mounir. 2015. « RF-LNA circuit synthesis using an array of artificial neural networks with constrained inputs ». In IEEE International Symposium on Circuits and Systems (ISCAS) (Lisbon, Portugal, May 24-27, 2015) pp. 573-576. Institute of Electrical and Electronics Engineers Inc..
Compte des citations dans Scopus : 11.

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Résumé

We describe a method for circuit synthesis that determines the parameter values by using a set of artificial neural networks (ANNs) that learn in sequence. Each ANN is optimized to output only one design parameter, and the latter constrains the learning/recall of its successor(s). Two competing ANN architectures are considered, the multilayer perceptron (MLP) and the radial basis functions (RBF) network, and each one has its internal parameters tuned by a genetic algorithm. The method was tested on the design of a radio-frequency, low-noise amplifier (RF-LNA) with ten design parameters to set, and it yielded one-hundred percent success rate in specifying the parameter values at five percent tolerance.

Type de document:	Compte rendu de conférence
ISBN:	02714310
Professeur:	Professeur Nabki, Frédéric
Affiliation:	Autres
Date de dépôt:	13 juill. 2016 18:08
Dernière modification:	15 déc. 2016 21:57
URI:	https://espace2.etsmtl.ca/id/eprint/13215

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