Model Reduction using a Frequency-Limited H2-Cost

Abstract

We propose a method for model reduction on a given frequency range, without having to specify input and output filter weights. The method uses a nonlinear optimization approach where we formulate a H2 like cost function which only takes the given frequency range into account. We derive a gradient of the proposed cost function which enables us to use off-the-shelf optimization software.