1 Signal Processing- EURASIP (SUBMITTED) A Minimum Error Entropy criterion with Self Adjusting

2 In this paper, we propose a Minimum Error Entropy with self adjusting step-size (MEE-SAS) as an alternative to the Minimum Error Entropy (MEE) algorithm for training adaptive systems. MEE-SAS has faster speed of convergence as compared to MEE algorithm for the same misadjustment. We attribute the self adjusting step size property of MEE-SAS to its changing curvature as opposed to MEE which has a constant curvature. Analysis of the curvature shows that MEE-SAS converges faster in noisy scenarios than noise free scenario, thus making it more suitable for practical applications as shown in our simulations. Finally in case of nonstationary environment, MEE-SAS loses its tracking ability due to the “flatness ” of the curvature near the optimal solution. We overcome this problem by proposing a switching MEE and MEE-SAS algorithm for non-stationary scenario which effectively combines the speed of MEE-SAS when far from the optimal solution with the tracking ability of MEE when near the solution. We demonstrate the performance of the switching algorithm in system identification in nonstationary environment