Neural Networks for Device and Circuit Modelling

  • P. B. L. Meijer
Conference paper
Part of the Lecture Notes in Computational Science and Engineering book series (LNCSE, volume 18)


The standard backpropagation theory for static feedforward neural networks can be generalized to include continuous dynamic effects like delays and phase shifts. The resulting non-quasistatic feedforward neural models can represent a wide class of nonlinear and dynamic systems, including arbitrary nonlinear static systems and arbitrary quasi-static systems as well as arbitrary lumped linear dynamic systems. When feedback connections are allowed, this extends to arbitrary nonlinear dynamic systems corresponding to equations of the general form \( f(x,\dot x,t) = 0 \). Extensions of learning algorithms to include combinations of time domain and frequency domain optimization lead to a semi-automatic modelling path from behaviour to simulation models. Model generators have been implemented for a range of existing analog circuit simulators, including support for the VHDL-AMS and Verilog-AMS language standards.


Circuit Modelling Feedforward Neural Network Neural Model Feedback Connection Dynamic Neural Network 
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-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • P. B. L. Meijer
    • 1
  1. 1.Philips Research LaboratoriesEindhovenThe Netherlands

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