Fuzzy Hidden Markov Model Using Aggregation Operators

Broumi Said; D. Nagarajan; J. Kavikumar; V. M. Gobinath

doi:10.47852/bonviewJCCE3202661

Authors

Broumi Said Faculty of Science Ben M’Sik, University of Hassan II, Morocco https://orcid.org/0000-0002-1334-5759
D. Nagarajan Department of Mathematics, Rajalakshmi Institute of Technology, India https://orcid.org/0000-0003-1411-532X
J. Kavikumar Department of Mathematics and Statistics, University Tun Hussein Onn Malaysia, Malaysia https://orcid.org/0000-0002-2314-4600
V. M. Gobinath Department of Mathematics, Rajalakshmi Institute of Technology, India

DOI:

https://doi.org/10.47852/bonviewJCCE3202661

Keywords:

triangular norms, aggregation operators, trapezoidal internal type-2 fuzzy number mathematical classification number: 60

Abstract

The Fuzzy Markov Model is a fascinating domain for dealing with ambiguity in real-world scenarios. In type-2 fuzzy set (T2F), it has an uncertainty footprint, and the region circumscribed by the lower and upper interval membership functions is uncertain. In fuzzy sets, triangular norms (t-norms) are a valuable tool for understanding the conjunction in fuzzy logic and, as a result, determining where fuzzy sets intersect. Norms and conforms in triangular operations that generalise logical conjunction and disjunction. They also provide a natural explanation for the conjunction and disjunction in mathematical fuzzy logic semantics. Fuzzy Frank t-norms have been used to verify this t-norm as there are many of the aggregation qualities of trapezoidal interval type-2 numbers (TpIT2FNs) because triangular norm meets the compatibility with Frank norms. Frank t-norms provide more flexibility and robustness; this requires more justification in the information fusion process than other t-norms. Previous works on not concentrate on Frank's norms. Other aggregation works on norms that are not flexible to get the solution. Because of that, the Frank norms are used for the hidden Markov model. We have also used them in the Viterbi method with TpIT2FNs for Fuzzy hidden Markov model in the staff selection process.

Received: 13 January 2023 | Revised: 21 March 2023 | Accepted: 4 April 2023

Conflicts of Interest

The authors declare that they have no conflicts of interest.

Data Availability Statement

Data sharing is not applicable to this article as no new data were created or analyzed in this study.

Author Contribution Statement

Broumi Said: Conceptualization, Validation, Writing – review & editing, Supervision. D. Nagarajan: Conceptualization, Methodology, Validation, Writing – original draft, Writing – review & editing, Supervision. J. Kavikumar: Conceptualization, Methodology, Writing – original draft, Writing – review & editing. V. M. Gobinath: Conceptualization, Writing – review & editing