# System Identification for Hybrid Systems using Neural Networks

arXiv: https://arxiv.org/abs/1911.12663 · HTML: https://mlsanity.com/paper/1911.12663/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Nov 2019.

## Built on by (most cited first, 2 of 2)

- [Assured Neural Network Architectures for Control and Identification of Nonlinear Systems](https://mlsanity.com/paper/2109.10298/) (2021)
- [A neural network-based approach to hybrid systems identification for control](https://mlsanity.com/paper/2404.01814/) (2024)

## Abstract (arXiv)

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.