Learning the Behavior of Dynamical Systems Form Examples
Abstract
This paper describes a general method for learning the behavior of dynamical systems. It can be used to control or predict their behavior. During a first step, the values of the state variables, and their interrelations are learned from the states which actually occur in the system. This data is organized in a topological map [Kohonen 84, 88]. Secondly, by looking at examples of the evolution of the system in time, the dynamical behavior is learned through a simple generalization process. This process constructs a vector field which describes the state transitions on the state space.
Cite
Text
Paredis. "Learning the Behavior of Dynamical Systems Form Examples." International Conference on Machine Learning, 1989. doi:10.1016/B978-1-55860-036-2.50042-4Markdown
[Paredis. "Learning the Behavior of Dynamical Systems Form Examples." International Conference on Machine Learning, 1989.](https://mlanthology.org/icml/1989/paredis1989icml-learning/) doi:10.1016/B978-1-55860-036-2.50042-4BibTeX
@inproceedings{paredis1989icml-learning,
title = {{Learning the Behavior of Dynamical Systems Form Examples}},
author = {Paredis, Jan},
booktitle = {International Conference on Machine Learning},
year = {1989},
pages = {137-140},
doi = {10.1016/B978-1-55860-036-2.50042-4},
url = {https://mlanthology.org/icml/1989/paredis1989icml-learning/}
}