Convergence, Consistence, and Stability Analysis of One-Step Methods for First-Order Intuitionistic Fuzzy Differential Equations

Convergence, Consistence, and Stability Analysis of One-Step Methods for First-Order Intuitionistic Fuzzy Differential Equations

Bouchra Ben Amma, Said Melliani, Lalla Saadia Chadli
Copyright: © 2022 |Pages: 23
DOI: 10.4018/IJFSA.302123
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Abstract

In this work, we consider the initial value problems in intuitionistic fuzzy ordinary differential equations. The one-step method for approximating the solution of these problems has been defined. The convergence, consistency, and stability of the difference method for approximating the solution of intuitionistic fuzzy differential equations are studied, and the local truncation error is defined. The accuracy and efficiency of the proposed concept are illustrated by solving some numerical examples.
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1. Introduction

The fuzzy set theory is completely described by its membership function. A membership function of a standard fuzzy set assigns to each element of the universe of discourse a number from the interval IJFSA.302123.m01 to indicate the degree of belongingness to the set under consideration. The degree of non-membership is just automatically equal to 1 minus the membership degree. However, a human being who expresses the degree of membership of a given element in a fuzzy set very often does not express the corresponding degree of non-membership as the complement to “1”. This reflects a well-known psychological fact that linguistic negation does not always identify with logical negation. There may be some hesitation about the belongingness and non-belongingness. This missing data or hesitation is accomplished by a set known as an intuitionistic fuzzy set. Atanassov (1983) proposed the concept of intuitionistic fuzzy sets (IFSs) as an extension of fuzzy sets introduced by Zadeh (1965). Atanassov (1986) explored the idea of fuzzy set theory by intuitionistic fuzzy set (IFS) theory. Several applications in IFSs are presented in (Robinson J. P., & Jeeva S., 2019; Muthukumar, P. & Gangadharan, S. S. 2018; Robinson, J. P. & Jeeva, S. 2017; Shreevastava, S., Tiwari, A.K. & Som, T. 2018; Talukdar, P. & Dutta, P. 2019; Tripathy, B. K., Sooraj, T. R., Mohanty, R. K. & Panigrahi, A. 2018; Yu, G.-F., Li, D. F., Qiu, J. M., & Ye, Y. F. 2017; An, J. & Li, D. 2019; An, J., Li, D. &. Nan, J. 2017; Li, D. & Wan, S. P. 2017; Li, D. & Liu, J. 2015; Li, D. 2014; Li. D. 2011; Li, D. 2010; Ren, H., Chen, H., Fei, W. & Li, D. 2017; Wei, A., Li, D., Jiang, B. & Lin, P. 2019; Wan, S. P. & Li., D. 2015; Melliani, S., Castillo, O. 2019).

The concept of intuitionistic fuzzy differential equations was first introduced by S. Melliani and L. S. Chadli (2000). The first step which included applicable definitions of intuitionistic fuzzy derivative and the intuitionistic fuzzy integral was followed by introducing intuitionistic fuzzy differential equations and establishing sufficient conditions for the existence of unique solutions to these equations using different concepts (Ben Amma, B., Melliani, S., & Chadli, L. S. 2018,2019; Melliani, S., Elomari, M., Atraoui M., & Chadli, L. S. 2015).

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