Designing a Bayesian Regularization Approach to Solve the Fractional Layla and Majnun System

The present work provides the numerical solutions of the mathematical model based on the fractional-order Layla and Majnun model (MFLMM). A soft computing stochastic-based Bayesian regularization neural network approach (BRNNA) is provided to investigate the numerical accomplishments of the MFLMM. The nonlinear system is classified into two dynamics, whereas the correctness of the BRNNA is observed through the comparison of results. Furthermore, the reducible performance of the absolute error improves the exactitude of the computational BRNNA. Twenty neurons have been chosen, along with the data statics of training 74% and 13%, for both authorization and testing. The consistency of the designed BRNNA is demonstrated using the correlation/regression, error histograms, and transition of state values in order to solve the MFLMM.

Automated summary

This study provides numerical solutions for the mathematical model based on the fractional-order Layla and Majnun model (MFLMM). It utilizes a soft computing stochastic-based Bayesian regularization neural network approach (BRNNA) to investigate the numerical achievements of the MFLMM. The BRNNA's accuracy is observed by comparing results, and the reducible performance of the absolute error improves the precision of the computational BRNNA. Twenty neurons were chosen, with training data statistics of 74% and 13% for authorization and testing. The consistency of the designed BRNNA is demonstrated using correlation/regression, error histograms, and the transition of state values to solve the MFLMM.