A Fixed-Point Minimum Error Entropy Algorithm

In this paper, we propose the fixed-point minimum error entropy (fixed-point MEE) as an alternative to the minimum error entropy (MEE) algorithm for training adaptive systems. The fixed-point algorithms are different from the gradient methods like MEE, and are proven to be faster, more stable and step-size free. This characteristic is due to the second order update similar to recursive least- squares (RLS) that tracks the Wiener solution with every update. We study the effect of design parameters, namely the forgetting factor, the window length, and the kernel size, on the convergence properties of the newly introduced recursive Fixed-Point MEE. Also, we test the performance of both the algorithms for two classic problems of system identification. Finally, we conclude that the Fixed-Point MEE performs better than MEE.