Novel Computing for the Delay Differential Two-Prey and One-Predator System  

作　　者：Prem Junsawang Zulqurnain Sabir Muhammad Asif Zahoor Raja Soheil Salahshour Thongchai Botmart Wajaree Weera 

机构地区：[1]Department of Statistics,Faculty of Science,Khon Kaen University,Khon Kaen,40002,Thailand [2]Department of Mathematics and Statistics,Hazara University,Mansehra,Pakistan [3]Future Technology Research Center,National Yunlin University of Science and Technology,Douliou,64002,Taiwan [4]Faculty of Engineering and Natural Sciences,Bahcesehir University,Istanbul,Turkey [5]Department of Mathematics,Faculty of Science,Khon Kaen University,Khon Kaen,40002,Thailand

出　　处：《Computers, Materials & Continua》2022年第10期249-263,共15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 B05F640088).

摘　　要：The aim of these investigations is to find the numerical performances of the delay differential two-prey and one-predator system.The delay differential models are very significant and always difficult to solve the dynamical kind of ecological nonlinear two-prey and one-predator system.Therefore,a stochastic numerical paradigm based artificial neural network(ANN)along with the Levenberg-Marquardt backpropagation(L-MB)neural networks(NNs),i.e.,L-MBNNs is proposed to solve the dynamical twoprey and one-predator model.Three different cases based on the dynamical two-prey and one-predator system have been discussed to check the correctness of the L-MBNNs.The statistic measures of these outcomes of the dynamical two-prey and one-predator model are chosen as 13%for testing,12%for authorization and 75%for training.The exactness of the proposed results of L-MBNNs approach for solving the dynamical two-prey and onepredator model is observed with the comparison of the Runge-Kutta method with absolute error ranges between 10−05 to 10−07.To check the validation,constancy,validity,exactness,competence of the L-MBNNs,the obtained state transitions(STs),regression actions,correlation presentations,MSE and error histograms(EHs)are also provided.

关 键 词：Delay differential model dynamical system PREY-PREDATOR Levenberg-Marquardt backpropagation MSE neural networks 

分 类 号：O17[理学—数学]