Unsupervised constrained neural network modeling of boundary value corneal model for eye surgery

Abstract

In this article, a numerical computing technique is developed for solving the nonlinear second order corneal shape model (CSM) using feed-forward artificial neural networks, optimized with particle swarm optimization (PSO) and active-set algorithms (ASA). The design parameter is approved initially with PSO known as global search, while for further prompt local refinements ASA is used. The performance of the design structure is scrutinized by solving a number of variants of CSM. The typical Adams numerical results are used for comparison of the proposed results, which establish the worth of the scheme in terms of convergence and accuracy. For more satisfaction, the present results are also compared with radial basis function (RBF) results. Moreover, statistical analysis based on mean absolute deviation, Theil's inequality coefficient and Nash Sutcliffe efficiency is presented (C) 2019 Published by Elsevier B.V.