  In this paper, we consider the learning of a Reduced-Order Linear
Parameter-Varying Model (ROLPVM) of a nonlinear dynamical system based on data.
This is achieved by a two-step procedure. In the first step, we learn a
projection to a lower dimensional state-space. In step two, an LPV model is
learned on the reduced-order state-space using a novel, efficient
parameterization in terms of neural networks. The improved modeling accuracy of
the method compared to an existing method is demonstrated by simulation
examples.
