Abstract
Abstract Neural networks are increasingly being used for nonlinear system identification due to their flexibility and the constantly growing ecosystem of available tools and methods. However, there are concerns about the interpretability of these models. With the aim of regaining interpretability, while retaining the generalization capabilities of these model structures, this work presents a method to compute low complexity piecewise affine (PWA) functions that closely approximate neural networks, and demonstrates it on a nonlinear system identification benchmark based on data gathered from a F-16 jet. The approach enables researchers to take advantage of both the applicability of machine learning methods to large, high-dimensional datasets, and the numerous controller synthesis techniques available for PWA systems.
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