Radial Basis Function Neural Network With Resilient Backpropagation for Solving Fractional‐Order Chaotic Virotherapy Dynamics

The purpose of this work is to solve the fractional‐order model of chaotic virotherapy dynamics by executing a neural network scheme. The chaotic virotherapy dynamics is divided into four categories: uninfected tumor cells, infected tumor cells, immune cells, and virus‐free cells. The implementation of fractional derivatives permits the unification of memory properties and long‐range dependencies in mathematical systems. The designed computing structure is performed by using a single layer and applying a radial basis activation function in the hidden layer with 12 neurons. Optimization is performed using the resilient backpropagation algorithm, which is one of the reliable schemes for the nonlinear models. The Adam optimizer is used to generate the dataset, which is divided into training as 77%, testing as 12%, and validation as 11%. The results of the fractional‐order model of chaotic virotherapy dynamics are presented for solving three variations of the fractional‐order values, while the accuracy of the results is validated through solution matching and best/optimal training values. In addition, the consistency of the designed solver is observed through the different states of transition, the regression coefficient, and the error histogram.