A Stochastic Bayesian Regularization Approach for the Fractional Food Chain Supply System with Allee Effects

Abstract

This motive of current research is to provide a stochastic platform based on the artificial neural networks (ANNs) along with the Bayesian regularization approach for the fractional food chain supply system (FFSCS) with Allee effects. The investigations based on the fractional derivatives are applied to achieve the accurate and precise results of FFSCS. The dynamical FFSCS is divided into special predator category P(η), top-predator class Q(η), and prey population dynamics R(η). The computing numerical performances for three different variations of the dynamical FFSCS are provided by using the ANNs along with the Bayesian regularization approach. The data selection for the dynamical FFSCS is selected for train as 78% and 11% for both test and endorsement. The accuracy of the proposed ANNs along with the Bayesian regularization method is approved using the comparison performances. For the rationality, ability, reliability, and exactness are authenticated by using the ANNs procedure enhanced by the Bayesian regularization method through the regression measures, correlation values, error histograms, and transition of state performances.