A radial basis deep neural network for the food chain supply system using the Allee impacts

The motive of this study is to design a novel stochastic radial basis deep neural network for the food chain (FC) supply system with Allee impacts. Two hidden layers are taken and the radial basis is performed in both hidden layers. The FC supply model is divided into three species: top-predator, predator, and the population of prey. Three different model cases are numerically presented, and the Bayesian regularization (BR) is used as an optimization. The novel process is designed by using the optimization based on the BR, radial basis along with 25 and 45 neurons in the first and second hidden layers. The dataset is obtained by the explicit Runge–Kutta scheme, which is used to decrease the mean square error by selecting the data as 68% for training, 20% for testing, and 12% for validation. The results overlap, small absolute errors and statistical measures are used to assess the correctness of the scheme for solving the FC supply system.