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In the paper a heuristic approach is introduced aimed first  of all to model supply chains which behavior may depend  on many parameters and their analytic description is many times problematic.  The proposed approach is based on identifying local vertex models  in the parameter space in form of multilayer perceptrons (MLPs). The parameter varying neural system might then be modeled as the convex combination of the identified vertex systems.  Depending on the dimension of the parameter space the number of identified vertex models might be large, therefore they reduction is crucial. In order to achieve this goal, first the vertex systems  are transformed into HOSVD based polytopic representation followed  by complexity reduction.

Polytopic Representation of Parameter Varying Neural Models for Loading Systems