For citation:
Uzentsova N. S. Simultaneous Approximation of Polynomial Functions and Its Derivatives by Feedforward Artificial Neural Networks with One Hidden Layer N. S.. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2013, vol. 13, iss. 2, pp. 78-82. DOI: 10.18500/1816-9791-2013-13-2-2-78-82, EDN: RHABLN
This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online:
25.05.2013
Full text:
(downloads: 137)
Language:
Russian
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UDC:
519.71, 519.651
EDN:
RHABLN
Simultaneous Approximation of Polynomial Functions and Its Derivatives by Feedforward Artificial Neural Networks with One Hidden Layer N. S.
Autors:
Uzentsova Nataliya Sergeevna, Saratov State University
Abstract:
In this paper we propose the algorithm for finding weights of feedforward artificial neural networks with one hidden layer to approximate polynomial functions and its derivatives with a given error. We use the rational sigmoidal function as a transfer function.
Key words:
References:
- Galyshkin A. Neironnye seti. Osnovy teorii [Neural Networks. The foundations of the theory]. Moscow, Goriachaia Liniia – Telekom, 2012 (in Russian).
- Haykin S. Neural Networks : A Comprehensive Foundation. Second ed., New Jersey, Prentice-Hall, Inc., 1999.
- Malakooti B., Zhou Y. Q. Approximating Polynomial Functions by Feedforward Artificial Neural Networks : Capacity Analysis and Design. Appl. Math. and Comp., 1998, vol. 90, pp. 27–52.
- Sidorov S. P. Ob oshibke priblizheniia algebraicheskikh polinomov neironnymi setiami priamogo rasprostraneniia [On the error of approximation of algebraical polynomials by means of artificial feedforward neural networks]. Neirokomp’iutery : razrabotka, primenenie, 2005, no. 5, pp. 13–17 (in Russian).
Received:
16.11.2012
Accepted:
20.04.2013
Published:
31.05.2013
Short text (in English):
(downloads: 65)
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