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

作　　者：Naret Ruttanaprommarin Zulqurnain Sabir Rafaél Artidoro Sandoval Nez Emad Az-Zo’bi Wajaree Weera Thongchai Botmart Chantapish Zamart 

机构地区：[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 Autonoma de Chota,Cajamarca,Peru [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

出　　处：《Computers, Materials & Continua》2023年第3期5915-5930,共16页计算机、材料和连续体（英文）

基　　金：This research received funding support from the NSRF via the Program Management Unit for Human Resources&Institutional Development,Research and Innovation[Grant Number B05F650018].

摘　　要：The current research aims to implement the numerical resultsfor the Holling third kind of functional response delay differential modelutilizing a stochastic framework based on Levenberg-Marquardt backpropagationneural networks (LVMBPNNs). The nonlinear model depends uponthree dynamics, prey, predator, and the impact of the recent past. Threedifferent cases based on the delay differential system with the Holling 3^(rd) type of the functional response have been used to solve through the proposedLVMBPNNs solver. The statistic computing framework is provided byselecting 12%, 11%, and 77% for training, testing, and verification. Thirteennumbers of neurons have been used based on the input, hidden, and outputlayers structure for solving the delay differential model with the Holling 3rdtype of functional response. The correctness of the proposed stochastic schemeis observed by using the comparison performances of the proposed and referencedata-based Adam numerical results. The authentication and precision ofthe proposed solver are approved by analyzing the state transitions, regressionperformances, correlation actions, mean square error, and error histograms.

关 键 词：Holling 3^(rd)type delay factor mathematical model neural networks levenberg-marquardt backpropagation 

分 类 号：O175[理学—数学]