NUMERICAL COMPUTING TO SOLVE THE NONLINEAR CORNEAL SYSTEM OF EYE SURGERY USING THE CAPABILITY OF MORLET WAVELET ARTIFICIAL NEURAL NETWORKS

In this study, a novel heuristic computing technique is presented to solve bioinformatics problem for the corneal shape model of eye surgery using Morlet wavelet artificial neural network optimized by the global search schemes, i.e. genetic algorithm (GA), local search technique, i.e. sequential quadratic programming (SQP) and the hybrid of GA-SQP. To measure the performance of the design network configuration, different cases based on nonlinear second-order differential equations governing the corneal model have been solved effectively. The numerical procedure of Adams method is implemented for the comparison purpose of the presented outcomes of the stochastic solver, which shows the wort Ii of the present scheme based on accuracy and convergence with negligible values of absolute error in the range 10(-7) to 10(-8). Furthermore, statistical measures are presented based on mean absolute error, root mean square error and coefficient of Theil's inequality which additionally endorsed consistently accurate performance of integrated intelligent computing framework for solving the corneal shape model.

Automated summary

A novel heuristic computing technique is proposed in this study to solve bioinformatics problem for the corneal shape model of eye surgery using Morlet wavelet artificial neural network optimized by the combination of global search schemes, i.e. genetic algorithm (GA), local search technique, i.e. sequential quadratic programming (SQP) and the hybrid of GA-SQP. The performance of the design network configuration is evaluated by effectively solving different cases based on the nonlinear second-order differential equations governing the corneal model. The presented outcomes of the stochastic solver are compared with the numerical procedure of Adams method, showing the worst value of the present scheme based on accuracy and convergence with negligible values of absolute error. Furthermore, statistical measures based on mean absolute error, root mean square error and coefficient of Theil's inequality consistently endorse the accurate performance of the integrated intelligent computing framework for solving the corneal shape model.