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,, 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.
CITATION STYLE
Meijer, P. B. L. (2001). Neural Networks for Device and Circuit Modelling (pp. 251–258). https://doi.org/10.1007/978-3-642-56470-3_25
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