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

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