![]() From a mathematical viewpoint, a neural network is now no longer a complicated nonlinear multidimensional function, but a system of nonlinear differential equations, for which one tries to tune the parameters in such a way that a good approximation of some specified behaviour is obtained. This is necessary for modelling the high-frequency behaviour of electronic components and circuits. To achieve this goal, the standard backpropagation theory for static feedforward neural networks has been extended to include continuous dynamic effects like, for instance, delays and phase shifts. Basically, the thesis covers the problem of constructing an efficient, accurate and numerically robust model, starting from behavioural data as obtained from measurements and/or simulations. Secondly, a transistor-level description of a (sub)circuit may be replaced by a much simpler macromodel, in order to obtain a major reduction of the overall simulation time. To begin with, it can help to significantly reduce the time needed to arrive at a sufficiently accurate simulation model for a new basic component - such as a transistor, in cases where a manual, physics-based, construction of a good simulation model would be extremely time-consuming. Such models can subsequently be applied in analogue simulations. ![]() The new approach allows for the generation of (macro-)models for highly nonlinear, dynamic and multidimensional systems, in particular electronic components and (sub)circuits. This thesis describes the main theoretical principles underlying new automatic modelling methods, generalizing concepts that originate from theories concerning artificial neural networks.
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