Neural network approach to intricate problems solving for ordinary differential equations
- Authors: Budkina E.M.1, Kuznetsov E.B.1, Lazovskaya T.V.2, Tarkhov D.A.3, Shemyakina T.A.3, Vasilyev A.N.3
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Affiliations:
- Moscow Aviation Institute
- Computer Center of the FEB RAS
- Peter the Great Saint-Petersburg Politechnical University
- Issue: Vol 26, No 2 (2017)
- Pages: 96-109
- Section: Article
- URL: https://journals.rcsi.science/1060-992X/article/view/194960
- DOI: https://doi.org/10.3103/S1060992X17020011
- ID: 194960
Cite item
Abstract
We consider the problems arising in the construction of the solutions of singularly perturbed differential equations. Usually, the decision of such problems by standard methods encounters significant difficulties of various kinds. The use of a common neural network approach is demonstrated in three model problems for ordinary differential equations. The conducted computational experiments confirm the effectiveness of this approach.
About the authors
E. M. Budkina
Moscow Aviation Institute
Author for correspondence.
Email: emb0909@rambler.ru
Russian Federation, Moscow
E. B. Kuznetsov
Moscow Aviation Institute
Email: emb0909@rambler.ru
Russian Federation, Moscow
T. V. Lazovskaya
Computer Center of the FEB RAS
Email: emb0909@rambler.ru
Russian Federation, Khabarovsk
D. A. Tarkhov
Peter the Great Saint-Petersburg Politechnical University
Email: emb0909@rambler.ru
Russian Federation, St. Petersburg
T. A. Shemyakina
Peter the Great Saint-Petersburg Politechnical University
Email: emb0909@rambler.ru
Russian Federation, St. Petersburg
A. N. Vasilyev
Peter the Great Saint-Petersburg Politechnical University
Email: emb0909@rambler.ru
Russian Federation, St. Petersburg
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