NUMERICAL INVESTIGATIONS OF A FRACTIONAL NONLINEAR DENGUE MODEL USING ARTIFICIAL NEURAL NETWORKS

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.