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in the range between minimum and maximum conductance of the experimental data, as needed for neural network simulations using these device models. For extrapolation situations, the functional type assumed for the trend model is very important, so if extrapolation needs to be considered, more sophistic...
Problem is it’s just not very good. The reason is that the extrapolation doesn’t know anything about the physics simulation. Extrapolation doesn’t know about collision with the floor so cubes extrapolate down through the floor and then spring back up to correct. Prediction doesn’t know abo...
The conventional TSK fuzzy inference system is extended in this section by allowing the interpolation and extrapolation of inference results. The extended system is thus workable with sparse rule bases, dense rule bases and imbalanced rule bases, which is termed as TSK+ inference system. 3.1 Modifie...
The basic idea of hierarchical interpolation is simple: We interpolate some frames first, and use them as key-frames for the next level of interpolations. See Fig.3for example. Each interpolation model\(\mathcal {M}_{a,b}\)referencesaframes into the past andbframes into the future. There...
Parameter Initialization The smaller the insensitive loss parameter ε, the higher the accuracy of the regression estimation is, but the increase of the number of support vectors may lead to the model being too complicated and without good extrapolation ability. When ε is bigger, the number of ...