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• W1478162562 abstract "AbstractIn the paper the recursive least squares method, in combining with general regression neural network, is applied for learning in a non-stationary environment. The orthogonal series-type kernel is applied to design the general regression neural networks. Sufficient conditions for convergence in probability are given and simulation results are presented.KeywordsIEEE TransactionMean Square ErrorRegression FunctionProbabilistic Neural NetworkGeneral Regression Neural NetworkThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
• W1478162562 created "2016-06-24" @default.
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• W1478162562 date "2012-01-01" @default.
• W1478162562 modified "2023-09-26" @default.
• W1478162562 title "Learning in a Non-stationary Environment Using the Recursive Least Squares Method and Orthogonal-Series Type Regression Neural Network" @default.
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• W1478162562 doi "https://doi.org/10.1007/978-3-642-31464-3_49" @default.
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