Neural Computing and Applications

, Volume 18, Issue 5, pp 485–494 | Cite as

Sequential modeling of a low noise amplifier with neural networks and active learning

  • Dirk Gorissen
  • Luciano De Tommasi
  • Karel Crombecq
  • Tom Dhaene
ISNN 2008

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.

Keywords

Global surrogate modeling Amplifier Active learning 

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Dirk Gorissen
    • 1
  • Luciano De Tommasi
    • 2
  • Karel Crombecq
    • 3
  • Tom Dhaene
    • 1
  1. 1.Department of Information Technology (INTEC)Ghent University, IBBTGentBelgium
  2. 2.University of Antwerp and NXP SemiconductorsEindhovenThe Netherlands
  3. 3.Department of Maths and Computer ScienceAntwerp UniversityAntwerpBelgium

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