Journal of Automation and Information Sciences
Published 12 issues per year
ISSN Print: 1064-2315
ISSN Online: 2163-9337
SJR:
0.173
SNIP:
0.588
CiteScore™::
2
Indexed in
Radial Basic Networks M-training by Asymmetric Influence Functions
Volume 44,
Issue 2, 2012,
pp. 48-64
DOI: 10.1615/JAutomatInfScien.v44.i2.50
ABSTRACT
We consider robust approach to radial basic networks training under the presence of measurement noise, which have asymmetric distributions. For minimization of the suggested asymmetric functionals the algorithms of Gauss−Newton and Levenberg−Marquardt are used. Estimation of noise parameters is done by the algorithm of stochastic approximation. Results of modeling, which confirm efficiency of the suggested approach, are stated.
KEY WORDS: robust approach, radial basic networks, the presence of measurement noise, asymmetric distribution, the of Gauss–Newton and Levenberg–Marquardt algorithms, estimation of noise parameters, algorithm of stochastic approximation, modeling, efficiency
CITED BY
-
Rudenko Oleg G., Bezsonov Oleksandr O., ADALINE Robust Multistep Training Algorithm, Control Systems and Computers, 3 (287), 2020. Crossref
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