System Identification for Hybrid Systems using Neural Networks

Abstract

With new advances in machine learning and in particular powerful learning libraries, we illustrate some of the new possibilities they enable in terms of nonlinear system identification. For a large class of hybrid systems, we explain how these tools allow for identification of complex dynamics using neural networks. We illustrate the method by examining the performance on a quad-rotor example.

References in corpus (1)

• DiffEqFlux.jl - A Julia Library for Neural Differential Equations

Cited by in corpus (1)

• Assured Neural Network Architectures for Control and Identification of Nonlinear Systems