Estimation of cyclostationary codebooks for kernel adaptive filtering

Abstract

A methodology based on kernel adaptive filtering termed DCKAF to support prediction tasks over one-dimensional time-series is proposed. DCKAF uses a linear combination of multiple codebooks to obtain the estimation from an input-output nonlinear mapping. This methodology employs a vector quantization based on statistic measures to check whether is necessary create a new codebook, then the nearest codebook to the current input sample is found. After that, codebooks are used to obtain the signal prediction at every instant, and evaluates if the current sample is added as a codeword or not as in traditional quantized kernel least mean square (QKLMS). Hence, DCKAF takes advantage of information learned on previous iterations to improve the system accuracy. The proposed methodology is tested on two one-dimensional time series and compared against QKLMS in terms of prediction accuracy. Obtained results show that DCKAF provides an effective way to predict time series improving prediction tasks.