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• W2139525055 abstract "A smoothed variant of the EM algorithm is given for simultaneous training of the first layer and the output layer globally in the normalized radial basis function (NRBF) nets and extended normalized RBF nets (ENRBF), together with a Bayesian Ying-Yang learning criterion for the selection of basis function numbers. Moreover, a hard-cut fast implementation and an adaptive algorithm have also been proposed for speeding up the training and for handling time varying in real time nonlinear signal learning and processing. A number of experiments are made on foreign exchange prediction and trading investments." @default.
• W2139525055 created "2016-06-24" @default.
• W2139525055 creator A5069972085 @default.
• W2139525055 date "2002-11-27" @default.
• W2139525055 modified "2023-09-27" @default.
• W2139525055 title "Adaptive RBF net algorithms for nonlinear signal learning with applications to financial prediction and investment" @default.
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• W2139525055 doi "https://doi.org/10.1109/icassp.1998.675474" @default.
• W2139525055 hasPublicationYear "2002" @default.
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