Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning

Probabilistic functional equations have been used to analyze various models in computational biology and learning theory. It is worth noting that they are linked to the symmetry of a system of functional equations’ transformation. Our objective is to propose a generic probabilistic functional equati...

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Main Authors: Ali Turab, Won-Gil Park, Wajahat Ali
Format: Article
Language:English
Published: MDPI AG 2021-07-01
Series:Symmetry
Subjects:
Online Access:https://www.mdpi.com/2073-8994/13/8/1313
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author Ali Turab
Won-Gil Park
Wajahat Ali
author_facet Ali Turab
Won-Gil Park
Wajahat Ali
author_sort Ali Turab
collection DOAJ
description Probabilistic functional equations have been used to analyze various models in computational biology and learning theory. It is worth noting that they are linked to the symmetry of a system of functional equations’ transformation. Our objective is to propose a generic probabilistic functional equation that can cover most of the mathematical models addressed in the existing literature. The notable fixed-point tools are utilized to examine the existence, uniqueness, and stability of the suggested equation’s solution. Two examples are also given to emphasize the significance of our findings.
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spelling doaj.art-5afdd42e599243439bfa9daa9c895eb52023-11-22T09:59:14ZengMDPI AGSymmetry2073-89942021-07-01138131310.3390/sym13081313Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of LearningAli Turab0Won-Gil Park1Wajahat Ali2Department of Mathematics and Statistics, Faculty of Science and Technology, Thammasat University Rangsit Center, Pathum Thani 12120, ThailandDepartment of Mathematics Education, College of Education, Mokwon University, Daejeon 35349, KoreaSchool of Science, Nanjing University of Science and Technology, Nanjing 210094, ChinaProbabilistic functional equations have been used to analyze various models in computational biology and learning theory. It is worth noting that they are linked to the symmetry of a system of functional equations’ transformation. Our objective is to propose a generic probabilistic functional equation that can cover most of the mathematical models addressed in the existing literature. The notable fixed-point tools are utilized to examine the existence, uniqueness, and stability of the suggested equation’s solution. Two examples are also given to emphasize the significance of our findings.https://www.mdpi.com/2073-8994/13/8/1313functional equationstabilityfixed points
spellingShingle Ali Turab
Won-Gil Park
Wajahat Ali
Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning
Symmetry
functional equation
stability
fixed points
title Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning
title_full Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning
title_fullStr Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning
title_full_unstemmed Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning
title_short Existence, Uniqueness, and Stability Analysis of the Probabilistic Functional Equation Emerging in Mathematical Biology and the Theory of Learning
title_sort existence uniqueness and stability analysis of the probabilistic functional equation emerging in mathematical biology and the theory of learning
topic functional equation
stability
fixed points
url https://www.mdpi.com/2073-8994/13/8/1313
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AT wajahatali existenceuniquenessandstabilityanalysisoftheprobabilisticfunctionalequationemerginginmathematicalbiologyandthetheoryoflearning