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Artificial Neural Network Approach for Solving Fuzzy Fractional order initial value problems under H-differentiability
  • Somayeh Ezadi,
  • Tofigh Allahviranloo
Somayeh Ezadi
Islamic Azad University Science and Research Branch Faculty of Basic Sciences
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Tofigh Allahviranloo
Bahcesehir Universitesi
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Abstract

This paper aims to solve the celebrated Fuzzy Fractional Differential Equations (FFDE) using an Artificial Neural Network (ANN) technique. Compared to the integer order differential equation, the proposed FFDE can better describe several real application problems of various physical systems. To accomplish the aforementioned aim, the error back propagation algorithm and a multi-layer feed forward neural architecture are utilized using the unsupervised learning in order to minimize the error function as well as the modification of the parameters such as weights and biases. By combining the initial conditions with the ANN, output provides an appropriate approximate solution of the proposed FFDE. Then, two illustrative examples are solved to confirm the applicability of the concept as well as to demonstrate both the precision and effectiveness of the developed method. By comparing with some traditional methods, the obtained results reveals a close match that confirms both accuracy and correctness of the proposed method.

Peer review status:ACCEPTED

09 Dec 2020Submitted to Mathematical Methods in the Applied Sciences
10 Dec 2020Submission Checks Completed
10 Dec 2020Assigned to Editor
13 Dec 2020Reviewer(s) Assigned
07 Jan 2021Review(s) Completed, Editorial Evaluation Pending
09 Jan 2021Editorial Decision: Revise Major
18 Jan 20211st Revision Received
18 Jan 2021Submission Checks Completed
18 Jan 2021Assigned to Editor
20 Jan 2021Reviewer(s) Assigned
20 Jan 2021Review(s) Completed, Editorial Evaluation Pending
20 Jan 2021Editorial Decision: Accept