Open AccessDIRECT AND INDIRECT CLASSIFICATION OF HIGH FREQUENCY LNA GAIN PERFORMANCE – A COMPARISON BETWEEN SVMS AND MLPS
Author(s) -
Peter Hung,
Seán McLoone,
Ronan Farrell
Publication year - 2014
Publication title -
computing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.184
H-Index - 11
eISSN - 2312-5381
pISSN - 1727-6209
DOI - 10.47839/ijc.8.1.653
Subject(s) - support vector machine , multilayer perceptron , computer science , perceptron , artificial intelligence , machine learning , noise (video) , key (lock) , low noise amplifier , pattern recognition (psychology) , amplifier , artificial neural network , telecommunications , bandwidth (computing) , computer security , image (mathematics)
The task of determining low noise amplifier (LNA) high-frequency performance in functional testing is as challenging as designing the circuit itself due to the difficulties associated with bringing high frequency signals offchip. One possible strategy for circumventing these difficulties is to inferentially estimate the high frequency performance measures from measurements taken at lower, more accessible, frequencies. This paper investigates the effectiveness of this strategy for classifying the high frequency gain of the amplifier, a key LNA performance parameter. An indirect Multilayer Perceptron (MLP) and direct support vector machine (SVM) classification strategy are considered. Extensive Monte-Carlo simulations show promising results with both methods, with the indirect MLP classifiers marginally outperforming SVMs.