Supervised neural learning for the predator-prey delay differential system of Holling form-III-Reference-Cited by-同舟云学术

Supervised neural learning for the predator-prey delay differential system of Holling form-III

Published:2022 Issue:11 Volume:7 Page:20126-20142
ISSN:2473-6988
Container-title:AIMS Mathematics
language:
Short-container-title:MATH

Author:

Ruttanaprommarin Naret¹,Sabir Zulqurnain²³,Said Salem Ben²,Raja Muhammad Asif Zahoor⁴,Bhatti Saira⁵,Weera Wajaree⁶,Botmart Thongchai⁶

Affiliation:

1. Department of Science and Mathematics, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakonnakhon Campus, Sakonnakhon 47160, Thailand

2. Department of Mathematical Sciences, United Arab Emirates University, P.O. Box 15551, Al Ain, UAE

3. Department of Mathematics, Hazara University, Mansehra, Pakistan

4. Future Technology Research Center, National Yunlin University of Science and Technology, 123 University Road, Section 3, Douliou, Yunlin 64002, Taiwan

5. Department of Mathematics, COMSATS University Islamabad, Abbottabad Campus, Abbottabad Pakistan

6. Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen 40002, Thailand

Abstract

<abstract> <p>The purpose of this work is to present the stochastic computing study based on the artificial neural networks (ANNs) along with the scaled conjugate gradient (SCG), ANNs-SCG for solving the predator-prey delay differential system of Holling form-III. The mathematical form of the predator-prey delay differential system of Holling form-III is categorized into prey class, predator category and the recent past effects. Three variations of the predator-prey delay differential system of Holling form-III have been numerical stimulated by using the stochastic ANNs-SCG procedure. The selection of the data to solve the predator-prey delay differential system of Holling form-III is provided as 13%, 12% and 75% for testing, training, and substantiation together with 15 neurons. The correctness and exactness of the stochastic ANNs-SCG method is provided by using the comparison of the obtained and data-based reference solutions. The constancy, authentication, soundness, competence, and precision of the stochastic ANNs-SCG technique is performed through the analysis of the correlation measures, state transitions (STs), regression analysis, correlation, error histograms (EHs) and MSE.</p> </abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Subject

General Mathematics

Reference56 articles.

1. J. D. Ferreira, C. A. T. Salazar, P. C. C. Tabares, Weak Allee effect in a predator-prey model involving memory with a hump, Nonlinear Anal.: Real World Appl., 14 (2013), 536–548. https://doi.org/10.1016/j.nonrwa.2012.07.014

2. M. Cavani, M. Farkas, Bifurcations in a predator-prey model with memory and diffusion. I: Andronov-Hopf bifurcation, Acta Math. Hung., 63 (1994), 213–229. https://doi.org/10.1007/bf01874129

3. M. Umar, Z. Sabir, M. A. Z. Raja, Intelligent computing for numerical treatment of nonlinear prey-predator models, Appl. Soft Comput., 80 (2019), 506–524. https://doi.org/10.1016/j.asoc.2019.04.022

4. Z. Sabir, T. Botmart, M. A. Z. Raja, W. Weera, An advanced computing scheme for the numerical investigations of an infection-based fractional-order nonlinear prey-predator system, Plos One, 17 (2022), 1–13. https://doi.org/10.1371/journal.pone.0265064

5. U. Ghosh, S. Pal, M. Banerjee, Memory effect on Bazykin's prey-predator model: Stability and bifurcation analysis, Chaos Solitons Fract., 143 (2021), 1–10. https://doi.org/10.1016/j.chaos.2020.110531

Cited by 4 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Artificial neural networks for the food web model;The European Physical Journal Plus;2024-04-29

2. Utilizing memory effects to enhance resilience in disease-driven prey-predator systems under the influence of global warming;Journal of Applied Mathematics and Computing;2023-11-30

3. Dynamics of a Predator–Prey Model with Impulsive Diffusion and Transient/Nontransient Impulsive Harvesting;Mathematics;2023-07-24

4. Modeling the dynamics of Diamondback Moth infestations on cabbage biomass;Informatics in Medicine Unlocked;2023