Solution of the Fuzzy Boundary Value Differential Equations Under Generalized Differentiability By Shooting Method

Bearing in mind the considerable importance of fuzzy differential equations (FDEs) in different fields of science and engineering, in this paper, nonlinear nth order FDEs are approximated, heuristically. The analysis is carried out on using Chebyshev neural network (ChNN), which is a type of single layer functional link artificial neural network (FLANN). Besides, explication of generalized Hukuhara differentiability (gH-differentiability) is also added for the nth order differentiability of fuzzy-valued functions. Moreover, general formulation of the structure of ChNN for the governing problem is described and assessed on some examples of nonlinear FDEs. In addition, comparison analysis of the proposed method with Runge-Kutta method is added and also portrayed the error bars that clarify the feasibility of attained solutions and validity of the method.

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Solution of the Fuzzy Boundary Value Differential Equations Under Generalized Differentiability By Shooting Method

Cited by 5 publications

References 23 publications

Solution of second order linear fuzzy ordinary differential equation by Lagrange multiplier method with application in mechanics

Solution of second order linear fuzzy ordinary differential equation by Lagrange multiplier method with application in mechanics

Solution of General Form of Fuzzy Second Order Boundary Value Problem

A Single Layer Functional Link Artificial Neural Network based on Chebyshev Polynomials for Neural Evaluations of Nonlinear Nth Order Fuzzy Differential Equations

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