Combined multiple random features least mean square algorithm for online applications

Abstract

Abstract The multikernel least mean square (MKLMS) algorithm is a classical algorithm of multikernel adaptive filters due to its simplicity. However, the linear growth network structure is a main challenge of MKLMS. To address this issue, a novel multiple random features least mean square (MRFLMS) algorithm is proposed by approximating multiple Gaussian kernels with the multiple random features method. In addition, a combined weight transfer strategy is adopted in MRFLMS to develop another combined multiple random features least mean square (CMRFLMS) algorithm to alleviate the influence of step‐size on filtering performance and convergence rate. CMRFLMS with a fixed dimensional network structure can provide comparable performance and faster convergence rate than MKLMS. Simulations on prediction of synthetic and real non‐linear system identification illustrate the superiorities of the proposed CMRFLMS algorithm from the aspects of filtering accuracy, convergence rate, and tracking performance.

Keywords

• combination of adaptive filters
• kernel adaptive filter
• multikernel least mean square
• multiple random features method