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Fuzzy Kolmogorov Complexity Based on a Classical Description

In this paper, we give a definition for fuzzy Kolmogorov complexity. In the classical setting, the Kolmogorov complexity of a single finite string is the length of the shortest program that produces this string. We define the fuzzy Kolmogorov complexity as the minimum classical description length of...

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Autor principal: Dai, Songsong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516500/
https://www.ncbi.nlm.nih.gov/pubmed/33285841
http://dx.doi.org/10.3390/e22010066
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author Dai, Songsong
author_facet Dai, Songsong
author_sort Dai, Songsong
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description In this paper, we give a definition for fuzzy Kolmogorov complexity. In the classical setting, the Kolmogorov complexity of a single finite string is the length of the shortest program that produces this string. We define the fuzzy Kolmogorov complexity as the minimum classical description length of a finite-valued fuzzy language through a universal finite-valued fuzzy Turing machine that produces the desired fuzzy language. The classical Kolmogorov complexity is extended to the fuzzy domain retaining classical descriptions. We show that our definition is robust, that is to say, the complexity of a finite-valued fuzzy language does not depend on the underlying finite-valued fuzzy Turing machine.
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spelling pubmed-75165002020-11-09 Fuzzy Kolmogorov Complexity Based on a Classical Description Dai, Songsong Entropy (Basel) Article In this paper, we give a definition for fuzzy Kolmogorov complexity. In the classical setting, the Kolmogorov complexity of a single finite string is the length of the shortest program that produces this string. We define the fuzzy Kolmogorov complexity as the minimum classical description length of a finite-valued fuzzy language through a universal finite-valued fuzzy Turing machine that produces the desired fuzzy language. The classical Kolmogorov complexity is extended to the fuzzy domain retaining classical descriptions. We show that our definition is robust, that is to say, the complexity of a finite-valued fuzzy language does not depend on the underlying finite-valued fuzzy Turing machine. MDPI 2020-01-04 /pmc/articles/PMC7516500/ /pubmed/33285841 http://dx.doi.org/10.3390/e22010066 Text en © 2020 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Dai, Songsong
Fuzzy Kolmogorov Complexity Based on a Classical Description
title Fuzzy Kolmogorov Complexity Based on a Classical Description
title_full Fuzzy Kolmogorov Complexity Based on a Classical Description
title_fullStr Fuzzy Kolmogorov Complexity Based on a Classical Description
title_full_unstemmed Fuzzy Kolmogorov Complexity Based on a Classical Description
title_short Fuzzy Kolmogorov Complexity Based on a Classical Description
title_sort fuzzy kolmogorov complexity based on a classical description
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7516500/
https://www.ncbi.nlm.nih.gov/pubmed/33285841
http://dx.doi.org/10.3390/e22010066
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