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- W2343766182 abstract "In this paper, we are going to propose an online radial basis function (RBF) neural network algorithm without any preprocessing step. Then a kernel principal component analysis (KPCA) is coupled with the proposed online RBF neural network algorithm. Indeed, the KPCA method is used as a preprocessing step to reduce the feature dimension which fed to the RBF neural network. Reducing memory requirements of the models makes RBF neural network training efficient and fast. These two proposed algorithms are applied, with success, for identification of a mobile robot position. The simulation results present that the used sigmoid function as a kernel, compared to other kernel functions, which gives an excellent model and a minimum mean square error." @default.
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- W2343766182 date "2016-04-28" @default.
- W2343766182 modified "2023-10-16" @default.
- W2343766182 title "Online identification using radial basis function neural network coupled with KPCA" @default.
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- W2343766182 doi "https://doi.org/10.1080/03081079.2016.1151421" @default.
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