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...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.展开更多
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].
文摘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.
文摘本文研究了东亚小花蝽4~5龄若虫捕食大豆蚜的功能反应和寻找效应。结果表明,在供试温度下其捕食功能反应符合Holling II型方程。在相同温度下,东亚小花蝽4~5龄若虫的捕食量随着猎物密度的增加而增大,搜寻效应随着猎物密度的增加而降低。在18~26℃,随着温度的升高,东亚小花蝽4~5龄若虫对大豆蚜的捕食量增加,而在26~34℃则有相反的趋势。在相同猎物密度条件下,随着东亚小花蝽4~5龄若虫密度的增大,其平均捕食量逐渐减少,捕食作用率E也相应地降低,捕食作用率E与东亚小花蝽4~5龄若虫密度P的关系为E=0.1249 P-0.1776,分摊竞争强度I与东亚小花蝽4~5龄若虫密度P的关系为I=0.3526 lg P+0.0152。
