A Novel Hybrid Kernel Adaptive Filtering Algorithm for Nonlinear Channel Equalization

In this paper, a novel kernel mixed error criterion (KMEC) algorithm is proposed for nonlinear system identification, which uses a combination of two different error schemes to implement a newly constructed cost function, which is realized by using a logarithmic squared error and a generalized maximum correntropy criterion (GMCC) to devise the KMEC algorithm. The proposed KMEC is derived in the context of the kernel adaptive filter and it provides good performance for identifying the nonlinear channels in different mixed noise environments in terms of the mean square error (MSE) at its steady-state and convergence performance.