The Nearest-Instance-Centroid-Estimation Kernel Recursive Least Squares Algorithms - Zhang, Haonan; Wang, Lin; Zhang, Tao; Wang, Shiyuan (IEEE transactions on circuits and systems. II, Express briefs, 2020)
The nearest-instance-centroid-estimation kernel least mean-square (NICE-KLMS) algorithm has been proposed to balance the time and space requirements in kernel adaptive filters. However, the minimum mean square error (MMSE) criterion used in NICE-KLMS leads to performance degradation in some nonlinear problems. In this brief, the NICE is developed under the least-squares errors in the kernel space, generating a novel NICE kernel recursive least squares (NICE-KRLS) algorithm for performance improvement of NICE-KLMS. The weight update form for the solution to the least-squares errors existing in NICE-KRLS is therefore obtained recursively. To obtain a sparsification network, the vector quantization is combined into NICE-KRLS for online applications. Simulations on chaotic time-series prediction validate the superiorities of the proposed NICE-KRLS and its sparsification version.