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

• Autores: Ruttanaprommarin, Naret, Sabir, Zulqurnain, Sandoval Núñez, Rafaél Artidoro, Az-Zo’bi, Emad A., Weera, Wajaree; T., Botmart, Thongchai, Zamart, Chantapish
• Formato: artículo
• Fecha de Publicación: 2022
• Institución: Universidad Nacional Autónoma de Chota
• Repositorio: UNACH-Institucional
• Lenguaje: inglés
• OAI Identifier: oai:repositorio.unach.edu.pe:20.500.14142/866
• Enlace del recurso: https://repositorio.unach.edu.pe/handle/20.500.14142/866; http://dx.doi.org/10.32604/cmc.2023.034362
• Nivel de acceso: acceso abierto
• Materia: numerical results; https://purl.org/pe-repo/ocde/ford#1.01.00

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.