Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method
In this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous s...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2021-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2021/5844741 |
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| _version_ | 1832555971254157312 |
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| author | Imran Khan Hakeem Ullah Hussain AlSalman Mehreen Fiza Saeed Islam Muhammad Shoaib Muhammad Asif Zahoor Raja Abdu Gumaei Farkhanda Ikhlaq |
| author_facet | Imran Khan Hakeem Ullah Hussain AlSalman Mehreen Fiza Saeed Islam Muhammad Shoaib Muhammad Asif Zahoor Raja Abdu Gumaei Farkhanda Ikhlaq |
| author_sort | Imran Khan |
| collection | DOAJ |
| description | In this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous stretching sheet (MHDFF BLPSS) problem. A dataset obtained by the fractional optimal homotopy asymptotic (FOHA) method is created as a simulated data simple for training (TR), validation (VD), and testing (TS) the proposed approach. The experiments are conducted by computing the results of mean-square-error (MSE), regression analysis (RA), absolute error (AE), and histogram error (HE) measures on the created dataset of FOHA solution. During the learning task, the parameters of trained model are adjusted by the efficacy of ANN backpropagation with the LMS (ANN-BLMS) approach. The ANN-BLMS performance of the modeled problem is verified by attaining the best convergence and attractive numerical results of evaluation measures. The experimental results show that the approach is effective for finding a solution of MHDFF BLPSS problem. |
| format | Article |
| id | doaj-art-1236b399af89415e99daaec7566cfb23 |
| institution | Kabale University |
| issn | 2314-8888 |
| language | English |
| publishDate | 2021-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-1236b399af89415e99daaec7566cfb232025-02-03T05:46:37ZengWileyJournal of Function Spaces2314-88882021-01-01202110.1155/2021/5844741Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic MethodImran Khan0Hakeem Ullah1Hussain AlSalman2Mehreen Fiza3Saeed Islam4Muhammad Shoaib5Muhammad Asif Zahoor Raja6Abdu Gumaei7Farkhanda Ikhlaq8Department of MathematicsDepartment of MathematicsDepartment of Computer ScienceDepartment of MathematicsDepartment of MathematicsDepartment of MathematicsFuture Technology Research CenterComputer Science DepartmentDepartment of ITIn this article, an effective computing approach is presented by exploiting the power of Levenberg-Marquardt scheme (LMS) in a backpropagation learning task of artificial neural network (ANN). It is proposed for solving the magnetohydrodynamics (MHD) fractional flow of boundary layer over a porous stretching sheet (MHDFF BLPSS) problem. A dataset obtained by the fractional optimal homotopy asymptotic (FOHA) method is created as a simulated data simple for training (TR), validation (VD), and testing (TS) the proposed approach. The experiments are conducted by computing the results of mean-square-error (MSE), regression analysis (RA), absolute error (AE), and histogram error (HE) measures on the created dataset of FOHA solution. During the learning task, the parameters of trained model are adjusted by the efficacy of ANN backpropagation with the LMS (ANN-BLMS) approach. The ANN-BLMS performance of the modeled problem is verified by attaining the best convergence and attractive numerical results of evaluation measures. The experimental results show that the approach is effective for finding a solution of MHDFF BLPSS problem.http://dx.doi.org/10.1155/2021/5844741 |
| spellingShingle | Imran Khan Hakeem Ullah Hussain AlSalman Mehreen Fiza Saeed Islam Muhammad Shoaib Muhammad Asif Zahoor Raja Abdu Gumaei Farkhanda Ikhlaq Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method Journal of Function Spaces |
| title | Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method |
| title_full | Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method |
| title_fullStr | Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method |
| title_full_unstemmed | Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method |
| title_short | Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method |
| title_sort | fractional analysis of mhd boundary layer flow over a stretching sheet in porous medium a new stochastic method |
| url | http://dx.doi.org/10.1155/2021/5844741 |
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