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A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III

Naret Ruttanaprommarin1, Zulqurnain Sabir2,3, Rafaél Artidoro Sandoval Núñez4, Emad Az-Zo’bi5, Wajaree Weera6, Thongchai Botmart6,*, Chantapish Zamart6

1 Department of Science and Mathematics, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakonnakhon Campus, Sakonnakhon, 47160, Thailand
2 Department of Mathematics and Statistics, Hazara University, Mansehra, Pakistan
3 Department of Mathematical Sciences, United Arab Emirates University, P. O. Box 15551, Al Ain, UAE
4 Universidad Nacional Autónoma de Chota, Cajamarca, Perú
5 Department of Mathematics and Statistics, Mutah University, Mutah-Al Karak-Jordan
6 Department of Mathematics, Faculty of Science, Khon Kaen University, Khon Kaen, 40002, Thailand

* Corresponding Author: Thongchai Botmart. Email: email

Computers, Materials & Continua 2023, 74(3), 5915-5930. https://doi.org/10.32604/cmc.2023.034362

Abstract

The current research aims to implement the numerical results for the Holling third kind of functional response delay differential model utilizing a stochastic framework based on Levenberg-Marquardt backpropagation neural networks (LVMBPNNs). The nonlinear model depends upon three dynamics, prey, predator, and the impact of the recent past. Three different cases based on the delay differential system with the Holling 3rd type of the functional response have been used to solve through the proposed LVMBPNNs solver. The statistic computing framework is provided by selecting 12%, 11%, and 77% for training, testing, and verification. Thirteen numbers of neurons have been used based on the input, hidden, and output layers structure for solving the delay differential model with the Holling 3rd type of functional response. The correctness of the proposed stochastic scheme is observed by using the comparison performances of the proposed and reference data-based Adam numerical results. The authentication and precision of the proposed solver are approved by analyzing the state transitions, regression performances, correlation actions, mean square error, and error histograms.

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APA Style
Ruttanaprommarin, N., Sabir, Z., Núñez, R.A.S., Az-Zo’bi, E., Weera, W. et al. (2023). A stochastic framework for solving the prey-predator delay differential model of holling type-iii. Computers, Materials & Continua, 74(3), 5915-5930. https://doi.org/10.32604/cmc.2023.034362
Vancouver Style
Ruttanaprommarin N, Sabir Z, Núñez RAS, Az-Zo’bi E, Weera W, Botmart T, et al. A stochastic framework for solving the prey-predator delay differential model of holling type-iii. Comput Mater Contin. 2023;74(3):5915-5930 https://doi.org/10.32604/cmc.2023.034362
IEEE Style
N. Ruttanaprommarin et al., "A Stochastic Framework for Solving the Prey-Predator Delay Differential Model of Holling Type-III," Comput. Mater. Contin., vol. 74, no. 3, pp. 5915-5930. 2023. https://doi.org/10.32604/cmc.2023.034362



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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