AGIAS Generalised Interval Arithmetic Simulator (AGIAS) is a specialised simulator whichuses affine arithmetic to model parameter variations. It uses a specialised root-findingalgorithm to simulate analogue circuits with parameter variations in one single simulationrun. This is a significant speed-up compared to the multiple runs needed by industrialisedsolutions such as Monte-Carlo (MC) or Worst-Case Analysis (WCA). Currently, AGIAScan simulate analogue circuits only under very specific conditions. In many cases, circuitscan only be simulated for certain operating points. If the circuits is to be evaluated in otheroperating points, the solver becomes numerically unstable and simulation fails. In thesecases, interval widths approach infinity.Behavioural modelling of analogue circuits was introduced by researchers working aroundlimitations of simulators. Most early approaches require expert knowledge and insight intothe circuit which is modelled. In recent years, Machine Learning techniques for automaticgeneration of behavioural models have made their way into the field. This thesis combinesMachine Learning techniques with affine arithmetic to include the effects of parametervariations into models.Support Vector Machines (SVMs) train two sets of parameters: one slope parameterand one offset parameter. These parameters are replaced by affine forms. Using these twoparameters allows affine SVMs to model effects of parameter variations with varying widths.Training requires additional information about maximum and minimum values in addition tothe nominal values in the data set. Based on these changes, affine ε Support Vector Machine(ε̂SVR) and ν Support Vector Machine (ν̂SVR) algorithms for regression are presented. Totrain the affine parameters directly and profit from the Sequential Minimal Optimisationalgorithm (SMO)’s selectivity, the SMO is extended to handle the new, larger optimisationproblems.The new affine SVMs are tested on analogue circuits that have been chosen based onwhether they could be simulated with AGIAS and how strongly non-linear their characteristicfunction is.