To decrease the network size of quantisedkernelleastmeansquare (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 quantisedkernelleastmeansquare (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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