Artificial Neural Network Approach for Solving Fuzzy Fractional order
initial value problems under H-differentiability
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.