Department of Mathematical Sciences, College of Sciences, United Arab Emirates University, P. O. Box 15551, Al Ain, United Arab Emirates.
Sci Rep. 2023 Apr 3;13(1):5402. doi: 10.1038/s41598-023-32497-5.
In this study, a fractional order mathematical model using the romantic relations of the Layla and Majnun is numerically simulated by the Levenberg-Marquardt backpropagation neural networks. The fractional order derivatives provide more realistic solutions as compared to integer order derivatives of the mathematical model based on the romantic relationship of the Layla and Majnun. The mathematical formulation of this model has four categories that are based on the system of nonlinear equations. The exactness of the stochastic scheme is observed for solving the romantic mathematical system using the comparison of attained and Adam results. The data for testing, authorization, and training is provided as 15%, 75% and 10%, along with the twelve numbers of hidden neurons. Furthermore, the reducible value of the absolute error improves the accuracy of the designed stochastic solver. To prove the reliability of scheme, the numerical measures are presented using correlations, error histograms, state transitions, and regression.
在这项研究中,使用莱拉和马俊恩的浪漫关系的分数阶数学模型通过勒文贝格-马夸特反向传播神经网络进行数值模拟。与基于莱拉和马俊恩浪漫关系的数学模型的整数阶导数相比,分数阶导数提供了更现实的解决方案。该模型的数学公式有四类,基于非线性方程组系统。使用比较获得的和 Adam 结果来观察随机方案求解浪漫数学系统的准确性。测试、授权和培训的数据分别为 15%、75%和 10%,以及 12 个隐藏神经元。此外,可约绝对值误差值提高了设计的随机求解器的准确性。为了证明方案的可靠性,使用相关性、误差直方图、状态转移和回归来呈现数值度量。