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- W2116632085 abstract "The performances of classical approximation methods and radial-basis function (RBF) neural networks in tracking of height density data are presented in the first part of the paper. The application of the different classical approximation methods in extraction of unknown biological processes from real measured data is considered in the second part of the paper. The advantages of implementation of RBF neural networks in the extraction of unknown processes are analyzes and illustrated with an example." @default.
- W2116632085 created "2016-06-24" @default.
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- W2116632085 date "2002-11-11" @default.
- W2116632085 modified "2023-09-26" @default.
- W2116632085 title "Radial basis function neural networks: in tracking and extraction of stochastic process in forestry" @default.
- W2116632085 cites W2098874108 @default.
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- W2116632085 doi "https://doi.org/10.1109/neurel.2000.902389" @default.
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