We consider the recurrent radial basis function network as a model of nonlinear dynamic system. On-line parameter and structure adaptation is unified under the framework of extended Kalman filter. The ability of adaptive system to deal with high observation noise, and the generalization ability of the resulting RRBF network are demonstrated in nonlinear system identification. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Todorović, B., Stanković, M., & Moraga, C. (2002). Extended Kalman filter trained recurrent radial basis function network in nonlinear system identification. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 819–824). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_133
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