Authors:
Asmir Vodenčarević
1
;
Alexander Maier
2
and
Oliver Niggemann
3
Affiliations:
1
University of Paderborn, Germany
;
2
OWL Universitiy of Applied Sciences, Germany
;
3
OWL Universitiy of Applied Sciences and Fraunhofer IOSB – Competence Center Industrial Automation, Germany
Keyword(s):
Stochastic Finite Automata, Machine Learning, Technical Systems.
Related
Ontology
Subjects/Areas/Topics:
Computational Learning Theory
;
Exact and Approximate Inference
;
Inductive Learning
;
Pattern Recognition
;
Regression
;
Stochastic Methods
;
Theory and Methods
Abstract:
Finite automata are used to model a large variety of technical systems and form the basis of important tasks
such as model-based development, early simulations and model-based diagnosis. However, such models are
today still mostly derived manually, in an expensive and time-consuming manner.
Therefore in the past twenty years, several successful algorithms have been developed for learning various
types of finite automata. These algorithms use measurements of the technical systems to automatically derive
the underlying automata models.
However, today users face a serious problem when looking for such model learning algorithm: Which algorithm
to choose for which problem and which technical system? This papers closes this gap by comparative
empirical analyses of the most popular algorithms (i) using two real-world production facilities and (ii) using
artificial datasets to analyze the algorithms’ convergence and scalability. Finally, based on these results, several
observations for choos
ing an appropriate automaton learning algorithm for a specific problem are given.
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