Design of Morlet wavelet neural network for solving the higher order singular nonlinear differential equations
Zulqurnain Sabir,Kashif Nisar,Muhammad Asif Zahoor Raja,Ag. Asri Bin Ag. Ibrahim,Joel J. P. C. Rodrigues,K.S. Al-Basyouni,S. R. Mahmoud,Danda B. Rawat +7 more
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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.read more
Citations
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Meyer wavelet neural networks to solve a novel design of fractional order pantograph Lane-Emden differential model
Zulqurnain Sabir,Muhammad Asif Zahoor Raja,Juan Luis García Guirao,Juan Luis García Guirao,Tareq Saeed +4 more
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.
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Neuron analysis through the swarming procedures for the singular two-point boundary value problems arising in the theory of thermal explosion
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Neuron analysis through the swarming procedures for the singular two-point boundary value problems arising in the theory of thermal explosion
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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.
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Solution of novel multi-fractional multi-singular Lane–Emden model using the designed FMNEICS
Zulqurnain Sabir,Muhammad Asif Zahoor Raja,Juan Luis García Guirao,Juan Luis García Guirao,Tareq Saeed +4 more
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.
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Novel design of Morlet wavelet neural network for solving second order Lane-Emden equation
Zulqurnain Sabir,Hafiz Abdul Wahab,Muhammad Umar,Mehmet Giyas Sakar,Muhammad Asif Zahoor Raja +4 more
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.
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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.
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Stochastic numerical technique for solving HIV infection model of CD4+ T cells
Muhammad Umar,Zulqurnain Sabir,Fazli Amin,Juan Luis García Guirao,Muhammad Asif Zahoor Raja,Muhammad Asif Zahoor Raja +5 more
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.