What question did this study set out to answer?

The study aims to develop a stochastic radial basis deep neural network for modeling the food chain supply system with Allee impacts.

January 24, 2026

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

Key Points

The study aims to develop a stochastic radial basis deep neural network for modeling the food chain supply system with Allee impacts.
Designed a neural network with two hidden layers using radial basis functions.
Divided the food chain model into top-predator, predator, and prey populations.
Applied Bayesian regularization for optimization.
Utilized the explicit Runge-Kutta scheme for data acquisition.
Allocated 68% of data for training, 20% for testing, and 12% for validation.
Achieved small absolute errors in the model outputs.
Demonstrated the effectiveness of Bayesian regularization in improving accuracy.
The model's performance was validated through statistical measures.

Abstract

The motive of current 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’s 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 twenty-five and forty-five neurons in first and second hidden layer. 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 endorsement. The results overlapping, small absolute error and statistical measures are used to perform the correctness of the scheme for solving the FC supply system.

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