کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
408799 | 679042 | 2009 | 4 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparison of generalization ability on solving differential equations using backpropagation and reformulated radial basis function networks
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موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
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چکیده انگلیسی
The gradient descent algorithms like backpropagation (BP) or its variations on multilayered feed-forward networks are widely used in many applications, especially on solving differential equations. Reformulated radial basis function networks (RBFN) are expected to have more accuracy in generalization capability than BP according to the regularization theory. We show how to apply the both networks to a specific example of differential equations and compare the capability of generalization and convergence. The experimental comparison of various approaches clarifies that reformulated RBFN shows better performance than BP for solving a specific example of differential equations.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 73, Issues 1–3, December 2009, Pages 115–118
Journal: Neurocomputing - Volume 73, Issues 1–3, December 2009, Pages 115–118
نویسندگان
Bumghi Choi, Ju-Hong Lee,