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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 1/2017

10.06.2016 | Original Article

Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model

verfasst von: Iftikhar Ahmad, Muhammad Asif Zahoor Raja, Muhammad Bilal, Farooq Ashraf

Erschienen in: Neural Computing and Applications | Sonderheft 1/2017

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Abstract

In the present study, stochastic numerical computing approach is developed by applying artificial neural networks (ANNs) to compute the solution of Lane–Emden type boundary value problems arising in thermodynamic studies of the spherical gas cloud model. ANNs are used in an unsupervised manner to construct the energy function of the system model. Strength of efficient local optimization procedures based on active-set (AS), interior-point (IP) and sequential quadratic programming (SQP) algorithms is used to optimize the energy functions. The performance of all three design methodologies ANN-AS, ANN-IP and ANN-SQP is evaluated on different nonlinear singular systems. The effectiveness of the proposed schemes in terms of accuracy and convergence is established from the results of statistical indicators.
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Metadaten
Titel
Neural network methods to solve the Lane–Emden type equations arising in thermodynamic studies of the spherical gas cloud model
verfasst von
Iftikhar Ahmad
Muhammad Asif Zahoor Raja
Muhammad Bilal
Farooq Ashraf
Publikationsdatum
10.06.2016
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe Sonderheft 1/2017
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-016-2400-y

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