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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| Format: | Article |
| Language: | English English |
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Springer London
2017
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| Online Access: | 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 |
| _version_ | 1848821097347153920 |
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| 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 |
| building | UMP Institutional Repository |
| collection | Online Access |
| 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. |
| first_indexed | 2025-11-15T02:19:56Z |
| format | Article |
| id | ump-20424 |
| institution | Universiti Malaysia Pahang |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-15T02:19:56Z |
| publishDate | 2017 |
| publisher | Springer London |
| recordtype | eprints |
| repository_type | Digital Repository |
| 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. (Published) https://link.springer.com/article/10.1007/s00521-016-2427-0 10.1007/s00521-016-2427-0 |
| 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 |
| title | 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_short | 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 |
| topic | GA Mathematical geography. Cartography |
| 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 |