Deep KKL: Data-driven Output Prediction for Non-Linear Systems
- URL: http://arxiv.org/abs/2103.12443v1
- Date: Tue, 23 Mar 2021 10:48:07 GMT
- Title: Deep KKL: Data-driven Output Prediction for Non-Linear Systems
- Authors: Steeven Janny, Vincent Andrieu, Madiha Nadri, Christian Wolf
- Abstract summary: We first define a general framework bringing together the necessary properties for the development of such an output predictor.
We propose a predictor structure based on the Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well into our general framework.
Our experiments show that our solution allows to obtain an efficient predictor over a subset of the observation space.
- Score: 7.5456680887905145
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: We address the problem of output prediction, ie. designing a model for
autonomous nonlinear systems capable of forecasting their future observations.
We first define a general framework bringing together the necessary properties
for the development of such an output predictor. In particular, we look at this
problem from two different viewpoints, control theory and data-driven
techniques (machine learning), and try to formulate it in a consistent way,
reducing the gap between the two fields. Building on this formulation and
problem definition, we propose a predictor structure based on the
Kazantzis-Kravaris/Luenberger (KKL) observer and we show that KKL fits well
into our general framework. Finally, we propose a constructive solution for
this predictor that solely relies on a small set of trajectories measured from
the system. Our experiments show that our solution allows to obtain an
efficient predictor over a subset of the observation space.
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