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Design of Morlet wavelet neural network for solving the higher order singular nonlinear differential equations

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TLDR
The Morlet wavelet neural networks is applied to discretize the higher order singular nonlinear differential equations to express the activation function using the mean square error to check the significance, efficacy and consistency of the designed MWNNs using the GA-IPM.
Abstract
The aim of this study is to present the numerical solutions of the higher order singular nonlinear differential equations using an advanced intelligent computational approach by manipulating the Morlet wavelet (MW) neural networks (NNs), global approach as genetic algorithm (GA) and quick local search approach as interior-point method (IPM), i.e., GA-IPM. MWNNs is applied to discretize the higher order singular nonlinear differential equations to express the activation function using the mean square error. The performance of the designed MWNNs using the GA-IPM is observed to solve three different variants based on the higher order singular nonlinear differential model to check the significance, efficacy and consistency of the designed MWNNs using the GA-IPM. Furthermore, statistical performances are provided to check the precision, accuracy and convergence of the present approach.

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Quasilinearization Approach to Nonlinear Problems in Physics with Application to Nonlinear ODEs

TL;DR: In this paper, the general conditions under which the quadratic, uniform and monotonic convergence in the quasilinearization method of solving nonlinear ordinary differential equations could be proved are formulated and elaborated.
Journal ArticleDOI

Quasilinearization approach to nonlinear problems in physics with application to nonlinear ODEs

TL;DR: In this paper, the general conditions under which the quadratic, uniform and monotonic convergence in the quasilinearization method of solving nonlinear ordinary differential equations could be proved are formulated and elaborated.
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