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Numerical Solutions for Fuzzy Fractional Differential Equations Using Logarithmic and Harmonic Means

Authors: P, Prakash; N, Nithyadevi;

Numerical Solutions for Fuzzy Fractional Differential Equations Using Logarithmic and Harmonic Means

Abstract

ABSTRACTFractional calculus is the study of the properties of integrals and derivatives of non-integer order.Fractional calculus evolved in the same way that classical calculus did. The concept of fractionaldifferential equations is expected to improve a selection of real-world applications, includingelectrical circuits, biology, biomechanics, electrochemistry, and electromagnetic processes. Basedon this the newly developed fractional differential equations have piqued the curiosity ofmathematicians and their applications, to the point that they are currently used in engineering todescribe a wide spectrum of physical and chemical processes. Understanding the qualitative andquantitative characteristics of complex physical phenomena necessitates the solution of nonlinearfractional differential equations in closed form (FDEs). Nonlinear themes include electricalengineering, mechanics, plasma physics, control theory, signal processing, finance, stochasticdynamical systems, and stochastic dynamical systems. In this study, we use a novel and extensibletechnique to analyse fuzzy fractional differential equations. The harmonic and logarithmic meanerror investigations are extensive. The results demonstrate that this approach can solve fuzzyfractional differential equations precisely.Keywords- Differential equations, Fuzzy fractional, Fuzzy numbers, Harmonic mean, Logarithmicmean

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citations
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
0
Average
Average
Average
Green