Title
Sequential modeling of a low noise amplifier with neural networks and active learning Sequential modeling of a low noise amplifier with neural networks and active learning
Author
Faculty/Department
Faculty of Sciences. Mathematics and Computer Science
Publication type
article
Publication
London ,
Subject
Computer. Automation
Source (journal)
Neural computing and applications. - London
Volume/pages
18(2009) :5 , p. 485-494
ISSN
0941-0643
ISI
000266665300010
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
The use of global surrogate models has become commonplace as a cost effective alternative for performing complex high fidelity computer simulations. Due to their compact formulation and negligible evaluation time, global surrogate models are very useful tools for exploring the design space, what-if analysis, optimization, prototyping, visualization, and sensitivity analysis. Neural networks have been proven particularly useful in this respect due to their ability to model high dimensional, non-linear responses accurately. In this article, we present the results of an extensive study on the performance of neural networks as compared to other modeling techniques in the context of active learning. We investigate the scalability and accuracy in function of the number design variables and number of datapoints. The case study under consideration is a high dimensional, parametrized low noise amplifier RF circuit block.
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