Quantized criterion-based kernel recursive least squares adaptive filtering for time series prediction

TL;DR

This paper introduces quantized GMEE-based kernel recursive least squares algorithms to enhance robustness and reduce computational complexity in time series prediction.

Contribution

It proposes two new KRLS-type algorithms, QKRMEE and QKRGMEE, combining quantized GMEE criteria to improve efficiency and robustness.

Findings

The proposed algorithms demonstrate improved robustness in simulations.

They achieve lower computational complexity compared to traditional KRLS.

Experimental results validate their effectiveness in real data scenarios.

Abstract

The robustness of the kernel recursive least square (KRLS) algorithm has recently been improved by combining them with more robust information-theoretic learning criteria, such as minimum error entropy (MEE) and generalized MEE (GMEE), which also improves the computational complexity of the KRLS-type algorithms to a certain extent. To reduce the computational load of the KRLS-type algorithms, the quantized GMEE (QGMEE) criterion, in this paper, is combined with the KRLS algorithm, and as a result two kinds of KRLS-type algorithms, called quantized kernel recursive MEE (QKRMEE) and quantized kernel recursive GMEE (QKRGMEE), are designed. As well, the mean error behavior, mean square error behavior, and computational complexity of the proposed algorithms are investigated. In addition, simulation and real experimental data are utilized to verify the feasibility of the proposed algorithms.