A COMPUTATIONAL APPROACH TO SOLVE THE NONLINEAR BIOLOGICAL PREY–PREDATOR SYSTEM

Abstract

This study is conducted to solve a nonlinear biological prey–predator system (NBPPS) using a novel design of the Levenberg–Marquardt backpropagation approach (LMBA). The LMBA-based supervised neural networks (SNNs) deal with three kinds of sample data, training, validation, and testing. The percentages for these data to solve three different cases of the NBPPS are selected: for training 75%, validation 10%, and testing 15%, respectively. The numerical performances of the Adams method are used for the reference dataset to solve the NBPPS. The obtained form of the numerical solutions of the NBPPS based on the SNNs along with LMBA is used to reduce the functions of mean square error (MSE). For the correctness, competence, and effectiveness of the proposed SNNs along with LMBA, the numerical procedures are proficient based on the proportional schemes and analyses in terms of MSE results, correlation, error histograms, and regression.