To decrease the network size of quantised kernel least mean square (qklms) dramatically, the qklms algorithm with an online learning vector strategy, which is named LV-qklms, is proposed in this Letter. The centres of...
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To decrease the network size of quantised kernel least mean square (qklms) dramatically, the qklms algorithm with an online learning vector strategy, which is named LV-qklms, is proposed in this Letter. The centres of the dictionary in LV-qklms are updated dynamically by the online learning vectors. Unlike qklms only updating the coefficient of the nearest centre to current input, LV-qklms updates the centres of the dictionary using the redundant input data accordingly. Thanks to the iterative learning vector, LV-qklms has a constantly changing quantisation area on the arrival of a new sample, and thus generates a superimposed quantisation area containing more redundant input data than qklms with a fixed quantisation area. In addition, the learning vector updated by a linear combination of input data and itself in the superimposed quantisation area guarantees the filtering accuracy of LV-qklms. Simulation results on the prediction of Mackey-Glass chaotic time series are presented to validate the effectiveness of LV-qklms.
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