A neural network training algorithm for singular perturbation boundary value problems

被引：0
|
作者：
T. E. Simos
Ioannis Th. Famelis
机构：
[1] Chengdu University of Information Technology,College of Applied Mathematics
[2] South Ural State University,Scientific and Educational Center “Digital Industry”
[3] China Medical University,Department of Medical Research, China Medical University Hospital
[4] Neijing Normal University,Data Recovery Key Laboratory of Sichun Province
[5] Democritus University of Thrace,Section of Mathematics, Department of Civil Engineering
[6] University of West Attica,microSENSES Laboratory, Department of Electrical and Electronics Engineering
来源：
Neural Computing and Applications | 2022年 / 34卷
关键词：
Computational intelligence; Neural Networks; Singular Perturbation Boundary Value Problems;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
A training algorithm for the Neural Network solution of Singular Perturbation Boundary Value Problems is presented. The solution is based on a single hidden layer feed forward Neural Network with a small number of neurons. The training algorithm adapts the training points grid so to be more tense in areas of the integration interval that solution has a layer or a peek. The algorithm automatically detects the areas of interest in the integration interval. The resulted Neural Network solutions are very accurate in a uniform way. The numerical tests in various test problems justify our arguments as the produced solutions prove to give smaller errors compare to their competitors.
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页码：607 / 615
页数：8
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