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Some Remarks on Strong Fuzzy Metrics and Strong Fuzzy Approximating Metrics with Applications in Word Combinatorics

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  • Raivis Bēts

    (Institute of Mathematics and Computer Science, University of Latvia, LV-1459 Riga, Latvia
    Department of Mathematics, University of Latvia, LV-1004 Riga, Latvia)

  • Alexander Šostak

    (Institute of Mathematics and Computer Science, University of Latvia, LV-1459 Riga, Latvia
    Department of Mathematics, University of Latvia, LV-1004 Riga, Latvia)

Abstract

Noticing that ordinary metrics do not present an adequate tool for the study of analytic problems of word combinatorics, as well as in the research of some problems related to theoretical computer science, we propose to use fuzzy metrics in this type of problems. Specifically, the so-called strong fuzzy metric seems to be more appropriate here. In the first part of the paper, we study some special classes of strong fuzzy metrics, topological and lattice properties of certain families of strong fuzzy metrics, and, more generally, strong k-fuzzy metrics. Noticing that one of the standard axioms of a strong fuzzy metric can be easily violated when applied in real situations, in the second part of the paper we introduce more general, approximating fuzzy metrics and illustrate their applicability with some numerical examples.

Suggested Citation

  • Raivis Bēts & Alexander Šostak, 2022. "Some Remarks on Strong Fuzzy Metrics and Strong Fuzzy Approximating Metrics with Applications in Word Combinatorics," Mathematics, MDPI, vol. 10(5), pages 1-20, February.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:5:p:738-:d:759112
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