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Neural Computing and Applications
Erschienen in: Neural Computing and Applications 5/2015

01.07.2015 | Original Article

Comparison of three unsupervised neural network models for first Painlevé Transcendent

verfasst von: Muhammad Asif Zahoor Raja, Junaid Ali Khan, Syed Muslim Shah, Raza Samar, Djilali Behloul

Erschienen in: Neural Computing and Applications | Ausgabe 5/2015

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Abstract

In this paper, a reliable soft computing framework is presented for the approximate solution of initial value problem (IVP) of first Painlevé equation using three unsupervised neural network models optimized with sequential quadratic programming (SQP). These mathematical models are constructed in the form of feed-forward architecture including log-sigmoid, radial base and tan-sigmoid activation functions in the hidden layers. The optimization of designed parameters for each model is performed with SQP, an efficient constraint optimization problem-solving algorithm. The designed methodology is tested on the IVP, and comparative study is carried out with standard solution based on numerical and analytical solvers. The accuracy, convergence and effectiveness of the schemes are validated on the given benchmark problem by large number of simulations and their comprehensive analysis.
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Metadaten
Titel
Comparison of three unsupervised neural network models for first Painlevé Transcendent
verfasst von
Muhammad Asif Zahoor Raja
Junaid Ali Khan
Syed Muslim Shah
Raza Samar
Djilali Behloul
Publikationsdatum
01.07.2015
Verlag
Springer London
Erschienen in
Neural Computing and Applications / Ausgabe 5/2015
Print ISSN: 0941-0643
Elektronische ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-014-1774-y

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