Numerical Solutions of the Wolbachia Invasive Model Using Levenberg-Marquardt Backpropagation Neural Network Technique

Abstract

The current study presents the numerical solutions of the Wolbachia invasive model (WIM) using the neural network Levenberg-Marquardt (NN-LM) backpropagation technique. The dynamics of the Wolbachia model is categorized into four classes, namely Wolbachia-uninfected aquatic mosquitoes (A*n), Wolbachia-uninfected adult female mosquitoes (Fn*), Wolbachia-infected aquatic mosquitoes (A*w), and Wolbachia-infected adult female mosquitoes (F*w). A reference dataset for the proposed NN-LM technique is created by solving the Wolbachia model using the Runge-Kutta (RK) numerical method. The reference dataset is used for validation, training, and testing of the proposed NN-LM technique for three different cases. The obtained numerical results from the proposed neural network technique are compared with the results obtained from the RK method for accuracy, correctness, and efficiency of the designed methodology. The validation of the proposed solution methodology is checked through the mean square error (MSE), error histograms, error plots, regression plots, and fitness plots.