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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 10/2018

25.02.2017 | Original Article

An efficient computational approach for time-fractional Rosenau–Hyman equation

verfasst von: Jagdev Singh, Devendra Kumar, Ram Swroop, Sunil Kumar

Erschienen in: Neural Computing and Applications | Ausgabe 10/2018

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Abstract

In this work, we concentrate on the analysis of the time-fractional Rosenau–Hyman equation occurring in the formation of patterns in liquid drops via q-homotopy analysis transform technique and reduced differential transform approach. The q-homotopy analysis transform algorithm can provide rapid convergent series by choosing the appropriate values of auxiliary parameters ħ and n at large domain. The reduced differential transform technique gives wider applicability due to reduction in computations and makes the calculation much simpler and easier. The proposed techniques are realistic and free from any assumption and perturbation for solving the time-fractional Rosenau–Hyman equation.
Vorheriger Artikel Adaptive neuro-fuzzy prediction of carbon monoxide emission from a clinical waste incineration plant
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Metadaten
Titel
An efficient computational approach for time-fractional Rosenau–Hyman equation
verfasst von
Jagdev Singh
Devendra Kumar
Ram Swroop
Sunil Kumar
Publikationsdatum
25.02.2017
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 10/2018
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-017-2909-8

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