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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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Citations
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Journal ArticleDOI

Meyer wavelet neural networks to solve a novel design of fractional order pantograph Lane-Emden differential model

TL;DR: The verification, perfection and authentication of the singular fractional order pantograph model using fractional Meyer computing solver is observed for different cases through comparative studies from the available exact solutions which endorsed its robustness, convergence and stability.
Journal ArticleDOI

Gudermannian neural networks using the optimization procedures of genetic algorithm and active set approach for the three-species food chain nonlinear model

TL;DR: In this article , the authors investigated the GNNs using the optimization procedures of genetic algorithm and active set approach (GA-ASA) to solve the three-species food chain nonlinear model.
Journal ArticleDOI

Solution of novel multi-fractional multi-singular Lane–Emden model using the designed FMNEICS

TL;DR: The explanations via the statistical measures validate the value of the designed stochastic solver FMW-NN-GAIPA, which is designed to characterize the novel model in the sagacity of mean squared error of objective function.
References
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Journal ArticleDOI

A new stochastic computing paradigm for the dynamics of nonlinear singular heat conduction model of the human head

TL;DR: The results of the proposed schemes are determined in terms of temperature profiles by considering variants of the problem with different Biot numbers, metabolic thermogenesis slope parameters and thermogenesis heat production factors.
Journal ArticleDOI

Rational Legendre Approximation for Solving some Physical Problems on Semi-Infinite Intervals

TL;DR: In this paper, a numerical technique for solving some physical problems on a semi-infinite interval is presented, which is based on a rational Legendre tau method and the operational matrices of derivative and product of rational linear Legendre functions are used to reduce the solution of these physical problems to the solutions of systems of algebraic equations.
Journal ArticleDOI

Novel design of Morlet wavelet neural network for solving second order Lane-Emden equation

TL;DR: A novel computational paradigm based on Morlet wavelet neural network optimized with integrated strength of genetic algorithm (GAs) and Interior-point algorithm (IPA) is presented for solving second order Lane–Emden equation.
Journal ArticleDOI

Intelligent computing for numerical treatment of nonlinear prey–predator models

TL;DR: A new computing paradigm is presented for evaluation of dynamics of nonlinear prey–predator mathematical model by exploiting the strengths of integrated intelligent mechanism through artificial neural networks, genetic algorithms and interior-point algorithm.
Journal ArticleDOI

Stochastic numerical technique for solving HIV infection model of CD4+ T cells

TL;DR: A comparison between the present results for different neurons-based models and the numerical values of the Runge–Kutta method reveals that the present intelligent computing techniques is trustworthy, convergent and robust.
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