Unsupervised neural networks for solving Troesch＇s problem

作     者：Muhammad Asif Zahoor Raja 

作者机构：Department of Electrical Engineering COMSATS Institute of Information Technology Attock Campus Attock Pakistan 

出 版 物：《Chinese Physics B》 (中国物理B（英文版）)

年 卷 期：2014年第23卷第1期

页      面：546-556页

核心收录：

学科分类：07[理学] 0805[工学-材料科学与工程（可授工学、理学学位）] 0704[理学-天文学] 0702[理学-物理学] 

主　　题：Troesch’s problem artificial neural network genetic algorithm hybrid methods 

摘      要：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.