NUMERICAL INVESTIGATIONS OF A FRACTIONAL NONLINEAR DENGUE MODEL USING ARTIFICIAL NEURAL NETWORKS
Zulqurnain Sabir, Muhammad Asif Zahoor Raja, Shumaila Javeed, Yolanda Guerrero–Sánchez
Abstract
The aim of this study is to perform the numerical investigations of a fractional nonlinear dengue model using artificial neuron networks (ANNs) along with the Levenberg–Marquardt backpropagation (LMB), i.e. ANNs. The fractional nonlinear dengue model is divided into five classes. The stochastic-based ANNs-LMB scheme is pragmatic on three variants of authentication, training and testing. The data magnitudes for three different variations based on the fractional nonlinear dengue model are selected as 80% for training, 10% for both testing and validation. The numerical procedures of the fractional nonlinear dengue model will be performed through ANNs-LMB and comparative investigations using the reference values that are calculated on the basis of Adams–Bashforth–Moulton scheme. The solution of the fractional nonlinear dengue model is obtained through the ANNs-LMB to reduce the mean square error (MSE). To authenticate the capability and efficiency of the proposed ANNs-LMB, the obtained numerical measures of correlation, MSE results, regression and error histograms (EHs) are provided.