Steady-State Mean-Square Error Analysis for Non-Negative Least Lncosh Algorithm

Lncosh cost function is regarded as a natural logarithm obtaining from a hyperbolic cosine function, which has drawn growing attention due to its robust to impulsive noise. In this brief, a nonnegative adaptive algorithm is proposed based on lncosh function, named as NNLlncosh, which is derived by incorporating the nonnegativity constraint into lncosh cost function to deal with a nonnegativity constraint optimization problem under impulsive system noises. The steady-state excess mean square error (EMSE) for the newly constructed NNLlncosh algorithm is presented. Obtained results from the computer simulation validate the theoretical analysis and verify the excellent characteristics of the NNLlncosh over various non-Gaussian system noises.