Stochastic numerical approach for solving second order nonlinear singular functional differential equation

被引：71
作者：
Sabir, Zulqurnain [1 ]
Wahab, Hafiz Abdul [1 ]
Umar, Muhammad [1 ]
Erdogan, Fevzi [2 ]
机构：
[1] Hazara Univ, Dept Math & Stat, Mansehra, Pakistan
[2] Yuzuncu Yil Univ, Fac Sci, Dept Math, Van, Turkey
来源：
APPLIED MATHEMATICS AND COMPUTATION | 2019年 / 363卷
关键词：
Functional differential equations; Artificial neural networks; Genetic algorithm; Nonlinear; Interior-point algorithm; Lane-Emden; INTERIOR-POINT ALGORITHM; BOUNDARY-VALUE-PROBLEMS; GENETIC ALGORITHMS; NEURAL-NETWORKS; MODEL; DESIGN; DYNAMICS; SIMULATION; PREDICTION; SYSTEMS;
D O I：
10.1016/j.amc.2019.124605
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
A new computational intelligence numerical scheme is presented for the solution of second order nonlinear singular functional differential equations (FDEs) using artificial neural networks (ANNs), global operator genetic algorithms (GAs), efficient local operator interio-rpoint algorithm (IPA), and the hybrid combination of GA-IPA. An unsupervised error function is assembled for the DDE optimized by ANNs using the hybrid combination of GA-IPA. Three kinds of the second order nonlinear singular DDEs have been solved numerically and compared their results with the exact solutions to authenticate the performance and exactness of the present designed scheme. Moreover, statistical analysis based on Mean absolute deviation, Theil's inequality coefficient and Nash Sutcliffe efficiency is also performed to validate the convergence and accuracy of the present scheme. (C) 2019 Elsevier Inc. All rights reserved.
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页数：11
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