RT Journal Article ID 2ac906a43f3ddc52 A1 Rudenko, Oleg G. A1 Bezsonov, Alexander A. T1 Radial Basic Networks M-training by Asymmetric Influence Functions JF Journal of Automation and Information Sciences JO JAI(S) YR 2012 FD 2012-03-06 VO 44 IS 2 SP 48 OP 64 K1 robust approach K1 radial basic networks K1 the presence of measurement noise K1 asymmetric distribution K1 the of Gauss–Newton and Levenberg–Marquardt algorithms K1 estimation of noise parameters K1 algorithm of stochastic approximation K1 modeling K1 efficiency AB 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. PB Begell House LK https://www.dl.begellhouse.com/journals/2b6239406278e43e,0edb0e3476542ab4,2ac906a43f3ddc52.html