ISSN:
1573-7675
Keywords:
machine discovery
;
machine learning
;
dynamical system identification
Source:
Springer Online Journal Archives 1860-2000
Topics:
Computer Science
Notes:
Abstract Machine discovery systems help humans to find natural laws from collections of experimentally collected data. Most of the laws found by existing machine discovery systems describe static situations, where a physical system has reached equilibrium. In this paper, we consider the problem of discovering laws that govern the behavior of dynamical systems, i.e., systems that change their state over time. Based on ideas from inductive logic programming and machine discovery, we present two systems, QMN and LAGRANGE, for discovery of qualitative and quantitative laws from quantitative (numerical) descriptions of dynamical system behavior. We illustrate their use by generating a variety of dynamical system models from example behaviors.
Type of Medium:
Electronic Resource
URL:
http://dx.doi.org/10.1007/BF00962824
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