Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks

In this article, the artificial intelligence techniques have been used for the solution of Falkner–Skan (FS) equations based on neural networks optimized with three methods including active set technique, sequential quadratic programming and genetic algorithms (GA) hybridization. Log-sigmoid activat...

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Main Authors:	Ahmad, Iftikhar, Ahmad, Siraj-ul-Islam, Bilal, Muhammad Qamar, Anwar, Nabeela
Format:	Article
Language:	English English
Published:	Springer London 2017
Subjects:	GA Mathematical geography. Cartography
Online Access:	http://umpir.ump.edu.my/id/eprint/20424/ http://umpir.ump.edu.my/id/eprint/20424/ http://umpir.ump.edu.my/id/eprint/20424/ http://umpir.ump.edu.my/id/eprint/20424/1/Stochastic%20Numerical%20Treatment%20for%20Solving%20Falkner%E2%80%93Skan%20Equations%20Using%20Feedforward%20Neural%20Networks.pdf http://umpir.ump.edu.my/id/eprint/20424/2/Stochastic%20Numerical%20Treatment%20for%20Solving%20Falkner%E2%80%93Skan%20Equations%20Using%20Feedforward%20Neural%20Networks%201.pdf

id	ump-20424
recordtype	eprints
spelling	ump-204242018-08-01T04:31:35Z http://umpir.ump.edu.my/id/eprint/20424/ Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks Ahmad, Iftikhar Ahmad, Siraj-ul-Islam Bilal, Muhammad Qamar Anwar, Nabeela GA Mathematical geography. Cartography In this article, the artificial intelligence techniques have been used for the solution of Falkner–Skan (FS) equations based on neural networks optimized with three methods including active set technique, sequential quadratic programming and genetic algorithms (GA) hybridization. Log-sigmoid activation function is used in artificial neural network architecture. The proposed techniques are applied to a number of cases for Falkner–Skan problems, and results were compared with GA hybrid results in all cases and were found accurate. The level of accuracy is examined through statistical analyses based on a sufficiently large number of independent runs. Springer London 2017-12 Article PeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/20424/1/Stochastic%20Numerical%20Treatment%20for%20Solving%20Falkner%E2%80%93Skan%20Equations%20Using%20Feedforward%20Neural%20Networks.pdf pdf en http://umpir.ump.edu.my/id/eprint/20424/2/Stochastic%20Numerical%20Treatment%20for%20Solving%20Falkner%E2%80%93Skan%20Equations%20Using%20Feedforward%20Neural%20Networks%201.pdf Ahmad, Iftikhar and Ahmad, Siraj-ul-Islam and Bilal, Muhammad Qamar and Anwar, Nabeela (2017) Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks. Neural Computing and Applications, 28. pp. 1131-1144. ISSN 0941-0643 https://link.springer.com/article/10.1007/s00521-016-2427-0 10.1007/s00521-016-2427-0
repository_type	Digital Repository
institution_category	Local University
institution	Universiti Malaysia Pahang
building	UMP Institutional Repository
collection	Online Access
language	English English
topic	GA Mathematical geography. Cartography
spellingShingle	GA Mathematical geography. Cartography Ahmad, Iftikhar Ahmad, Siraj-ul-Islam Bilal, Muhammad Qamar Anwar, Nabeela Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks
description	In this article, the artificial intelligence techniques have been used for the solution of Falkner–Skan (FS) equations based on neural networks optimized with three methods including active set technique, sequential quadratic programming and genetic algorithms (GA) hybridization. Log-sigmoid activation function is used in artificial neural network architecture. The proposed techniques are applied to a number of cases for Falkner–Skan problems, and results were compared with GA hybrid results in all cases and were found accurate. The level of accuracy is examined through statistical analyses based on a sufficiently large number of independent runs.
format	Article
author	Ahmad, Iftikhar Ahmad, Siraj-ul-Islam Bilal, Muhammad Qamar Anwar, Nabeela
author_facet	Ahmad, Iftikhar Ahmad, Siraj-ul-Islam Bilal, Muhammad Qamar Anwar, Nabeela
author_sort	Ahmad, Iftikhar
title	Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks
title_short	Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks
title_full	Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks
title_fullStr	Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks
title_full_unstemmed	Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks
title_sort	stochastic numerical treatment for solving falkner–skan equations using feedforward neural networks
publisher	Springer London
publishDate	2017
url	http://umpir.ump.edu.my/id/eprint/20424/ http://umpir.ump.edu.my/id/eprint/20424/ http://umpir.ump.edu.my/id/eprint/20424/ http://umpir.ump.edu.my/id/eprint/20424/1/Stochastic%20Numerical%20Treatment%20for%20Solving%20Falkner%E2%80%93Skan%20Equations%20Using%20Feedforward%20Neural%20Networks.pdf http://umpir.ump.edu.my/id/eprint/20424/2/Stochastic%20Numerical%20Treatment%20for%20Solving%20Falkner%E2%80%93Skan%20Equations%20Using%20Feedforward%20Neural%20Networks%201.pdf
first_indexed	2023-09-18T22:29:26Z
last_indexed	2023-09-18T22:29:26Z
_version_	1777416185952862208

Stochastic numerical treatment for solving Falkner–Skan equations using feedforward neural networks

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