A novel heuristic Morlet wavelet neural network procedure to solve the delay differential perturbed singular model

Abstract

This study designs the Morlet wavelet neural network (MWNN) for the numerical performance of the second-order delay differential perturbed singular model (DD-PSM). These stiff singular models are always challenging for the research community to numerically present their results. The DD-PSM is used as an objective function, and its boundary conditions are assembled and then optimised using the computing hybrid proficiency of the global genetic algorithm (GA) and local active-set approach (ASA). Details of the singularity, shape factor, perturbed and delay terms based on the DD-PSM are also provided. Three problems of the DD-PSM are presented and numerically solved using the MWNN–GA–ASA. The precision of the MWNN–GA–ASA is studied by comparing the proposed solution-based DD-PSM and exact solutions. Moreover, a comparison of the MWNN with the Meyer wavelet neural network is presented. The reliability, convergence, correctness and constancy of the numerical scheme are observed by different statistical performances.