Mixture structure of kernel adaptive filters for improving the convergence characteristics

In this paper, we propose a mixture structure of the linear and kernel adaptive fiilters for improving the convergence characteristics of the kernel normalized least mean square (KLMS) adaptive algorithm. The proposed method is based on the concept of the affine constrained mixture structure for the linear normalized LMS adaptive filters which uses the more than two adaptive filters concurrently. We derive the proposed structure, and its implementation method. We confirm the effectiveness of the proposed method through the computer simulations.