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Improvement of ANNs Performance to Generate Fitting Surfaces for Analog CMOS Circuits

  • José Ángel Díaz-Madrid
  • Pedro Monsalve-Campillo
  • Juan Hinojosa
  • María Victoria Rodellar Biarge
  • Ginés Doménech-Asensi
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4528)

Abstract

One of the typical applications of neural networks is based on their ability to generate fitting surfaces. However, for certain problems, error specifications are very restrictive, and so, the performance of these networks must be improved. This is the case of analog CMOS circuits, where models created must provide an accuracy which some times is difficult to achieve using classical techniques. In this paper we describe a modelling method for such circuits based on the combination of classical neural networks and electromagnetic techniques. This method improves the precision of the fitting surface generated by the neural network and keeps the training time within acceptable limits.

Keywords

ANNs Performance Coarse Model Classical Neural Network Fitting Surface CMOS Circuit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin Heidelberg 2007

Authors and Affiliations

  • José Ángel Díaz-Madrid
    • 1
  • Pedro Monsalve-Campillo
    • 2
  • Juan Hinojosa
    • 2
  • María Victoria Rodellar Biarge
    • 3
  • Ginés Doménech-Asensi
    • 2
  1. 1.Fraunhofer Institute, Erlangen 91058Germany
  2. 2.Universidad Politécnica de Cartagena, Cartagena 30202Spain
  3. 3.Universidad Politécnica de Madrid, Campus de Montegancedo, Madrid 28660Spain

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