An Artificial Neural Network Solution To A Higher-Order Fractional Linear Integro-Differential Problem Using Deep Learning

Abstract

To put it another way, the idea of organizing an attention was drawn to the best iterative first-order approach for estimating solutions to the origin fractional issue.Furthermore, a few computer simulation models show how accurate and useful the suggested iterative method is. When compared to traditional methods, the exceptional achieved numerical figures easily demonstrate the efficiency and skill of artificial neural network techniques.One new and exciting topic of study in the fields of machine learning and numerical analysis is the use of ANN to solve fractional higher-order linear integro-differential equations (IHODEs).. Fractional-order integro-differential equations are extensions of classical differential equations, where derivatives are of non-integer order, often incorporating memory and hereditary properties, which makes them useful in modeling complex systems in various fields, such as physics, engineering, and finance