Solving Fractional Differential Equations by Using Triangle Neural Network. (14th April 2021)

• Record Type: Journal Article
• Authors: Gao, Feng; Dong, Yumin; Chi, Chunmei
• Other Names: Zhang Xinguang Academic Editor.
• Is Part Of: Journal of function spaces. Volume 2021(2021)
• Publication Date: 2021-04-14
• Subjects: Function spaces -- Periodicals; 515.7305
• DOI: 10.1155/2021/5589905
• Languages: English
• ISSNs: 2314-8896

Abstract

Abstract : In this paper, numerical methods for solving fractional differential equations by using a triangle neural network are proposed. The fractional derivative is considered Caputo type. The fractional derivative of the triangle neural network is analyzed first. Then, based on the technique of minimizing the loss function of the neural network, the proposed numerical methods reduce the fractional differential equation into a gradient descent problem or the quadratic optimization problem. By using the gradient descent process or the quadratic optimization process, the numerical solution to the FDEs can be obtained. The efficiency and accuracy of the presented methods are shown by some numerical examples. Numerical tests show that this approach is easy to implement and accurate when applied to many types of FDEs.