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

T. Saeed (), Juan L. G. Guirao, Zulqurnain Sabir (), Hamed H. Alsulami () and Yolanda Guerrero Sã Nchez
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T. Saeed: ��Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia
Juan L. G. Guirao: ��Department of Applied Mathematics and Statistics, Technical University of Cartagena, Hospital de Marina, 30203 Cartagena, Spain‡Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia
Zulqurnain Sabir: Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
Hamed H. Alsulami: ��Department of Mathematics, Faculty of Science, King Abdulaziz University, P. O. Box 80203, Jeddah 21589, Saudi Arabia
Yolanda Guerrero Sã Nchez: �Departamento de Anatomía Humana y Psicobiologia, University of Murcia, 30100 Murcia, Spain

FRACTALS (fractals), 2022, vol. 30, issue 10, 1-10

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.

Keywords: Supervised Neural Networks; Biological Prey–Predator System; Levenberg–Marquardt Backpropagation; Reference Dataset; Numerical Results (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0218348X22402678

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