Bibliographic Details
| Title: |
A machine learning approach to synchronization of automata. |
| Authors: |
Podolak, Igor1 podolak@ii.uj.edu.pl, Roman, Adam1 roman@ii.uj.edu.pl, Szykuła, Marek2 msz@cs.uni.wroc.pl, Zieliński, Bartosz1 zielinsb@ii.uj.edu.pl |
| Source: |
Expert Systems with Applications. May2018, Vol. 97, p357-371. 15p. |
| Subjects: |
Machine learning, Big data, Data mining, Numerical analysis, Mathematical analysis |
| Abstract: |
We present a novel method to predict the length of the shortest synchronizing words of a finite automaton by applying the machine learning approach. We introduce several so-called automata features which depict the structure of an automaton, and use them with machine learning algorithms. The article discusses effectiveness of the machine learning approach in predicting the length of the shortest synchronizing words. We also examine the impact of particular features on this length, which may be helpful in methods of constructing automata as models of real systems, algorithms finding synchronizing words, and further theoretical research on synchronizing automata and the Černý conjecture. [ABSTRACT FROM AUTHOR] |
|
Copyright of Expert Systems with Applications is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) |
| Database: |
Engineering Source |
