Using ANNs Approach for Solving Fractional Order Volterra Integro-differential Equations

Abstract

Indeed, interesting properties of artificial neural networks approach made this non-parametric model a powerful tool in solving various complicated mathematical problems. The current research attempts to produce an approximate polynomial solution for special type of fractional order Volterra integrodifferential equations. The present technique combines the neural networks approach with the power series method to introduce an efficient iterative technique. To do this, a multi-layer feed-forward neural architecture is depicted for constructing a power series of arbitrary degree. Combining the initial conditions with the resulted series gives us a suitable trial solution. Substituting this solution instead of the unknown function and employing the least mean square rule, converts the origin problem to an approximated unconstrained optimization problem. Subsequently, the resulting nonlinear minimization problem is solved iteratively using the neural networks approach. For this aim, a suitable three-layer feed-forward neural architecture is formed and trained using a back-propagation supervised learning algorithm which is based on the gradient descent rule. In other words, discretizing the differential domain with a classical rule produces some training rules. By importing these to designed architecture as input signals, the indicated learning algorithm can minimize the defined criterion function to achieve the solution series coefficients. Ultimately, the analysis is accompanied by two numerical examples to illustrate the ability of the method. Also, some comparisons are made between the present iterative approach and another traditional technique. The obtained results reveal that our method is very effective, and in these examples leads to the better approximations.

Artificial neural network approach for solving fractional order applied problems

“…employed neural network and Broyden-Fletcher-Goldfarb-Shanno (BFGS) optimization technique to solve linear and nonlinear FDEs. Jafarian et al [32] have applied artificial neural network model for approximate polynomial solution of special type of fractional order Volterra integro differential equations. The neural network training and cosine basis functions with adjustable parameters have been presented by Qu and Liu [33] for solving single and the systems of coupled fractional order differential equations.…”

Fractional Chebyshev functional link neural network‐optimization method for solving delay fractional optimal control problems with Atangana‐Baleanu derivative

“…Effati and Pakdaman used multilayer neural networks for estimating the solution of fuzzy differential equations and optimal control problems In particular, a MLP approach is used for solving fractional‐order Volterra integro‐differential. equations and fractional‐order initial value problems . It should be noted that, in a MLP construction, the hidden neurons play the main role to handle nonlinear input output mapping.…”

A new spectral method based on two classes of hat functions for solving systems of fractional differential equations and an application to respiratory syncytial virus infection

“…So, in most cases, the exact solution is not known, and it is necessary to find a numerical approximation. Therefore, many researchers have worked on numerical methods to obtain some numerical solutions of fractional dynamic systems (see, e.g., [Ali et al (2019), Jafarian et al (2018), Jafarian et al (2017), El-Sayed and Agarwal (2019), Nigmatullin and Agarwal (2019)]).…”