Unsupervised neural networks for solving Troesch’s problem |
| |
| Authors: | Muhammad Asif Zahoor Raja |
| |
| Affiliation: | Department of Electrical Engineering, COMSATS Institute of Information Technology, Attock Campus, Attock, Pakistan |
| |
| Abstract: | In this study, stochastic computational intelligence techniques are presented for the solution of Troesch's boundary value problem. The proposed stochastic solvers use the competency of a feed-forward artificial neural network for mathematical modeling of the problem in an unsupervised manner, whereas the learning of unknown parameters is made with local and global optimization methods as well as their combinations. Genetic algorithm(GA) and pattern search(PS) techniques are used as the global search methods and the interior point method(IPM) is used for an efficient local search. The combination of techniques like GA hybridized with IPM(GA-IPM) and PS hybridized with IPM(PS-IPM) are also applied to solve different forms of the equation. A comparison of the proposed results obtained from GA, PS, IPM, PS-IPM and GA-IPM has been made with the standard solutions including well known analytic techniques of the Adomian decomposition method, the variational iterational method and the homotopy perturbation method. The reliability and effectiveness of the proposed schemes, in term of accuracy and convergence, are evaluated from the results of statistical analysis based on sufficiently large independent runs. |
| |
| Keywords: | Troesch’ s problem artificial neural network genetic algorithm hybrid methods |
| 本文献已被 CNKI 等数据库收录！ |
| 点击此处可从《中国物理 B》浏览原始摘要信息 |
|
点击此处可从《中国物理 B》下载免费的PDF全文 |
|
