Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method
Fractional Analysis of MHD Boundary Layer Flow over a Stretching Sheet in Porous Medium: A New Stochastic Method
复制标题
DOI：
10.1155/2021/5844741
复制
发表时间：
2021-11-30
影响因子：
1.9
通讯作者：
Ikhlaq, Farkhanda
中科院分区：
文献类型：
--
作者：
Khan, Imran;Ullah, Hakeem;Ikhlaq, Farkhanda
在这篇文章中，提出了一种有效的计算方法，通过开发的功能Levenberg-Marquardt计划（LMS）的人工神经网络（ANN）的反向传播学习任务。本文提出了求解多孔拉伸薄板边界层磁流体动力学（MHD）分数流（MHDFF BLPSS）问题的方法。通过分数最优同伦渐近（FOHA）方法获得的数据集被创建为简单的训练（TR），验证（VD）和测试（TS）所提出的方法的模拟数据。通过计算FOHA解决方案的创建数据集上的均方误差（MSE），回归分析（RA），绝对误差（AE）和直方图误差（HE）措施的结果进行实验。在学习过程中，训练好的模型的参数是通过使用LMS（ANN-BLMS）方法的ANN反向传播的功效来调整的。ANN-BLMS性能的建模问题进行了验证，达到最佳的收敛性和有吸引力的数值结果的评价措施。实验结果表明，该方法是有效的MHDFF BLPSS问题的解决方案。
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.