Mean square convergence analysis for kernel least mean square algorithm

@article{Chen2012MeanSC,
  title={Mean square convergence analysis for kernel least mean square algorithm},
  author={Badong Chen and Songlin Zhao and Pingping Zhu and Jos{\'e} Carlos Pr{\'i}ncipe},
  journal={Signal Processing},
  year={2012},
  volume={92},
  pages={2624-2632}
}
In this paper, we study the mean square convergence of the kernel least mean square (KLMS). The fundamental energy conservation relation has been established in feature space. Starting from the energy conservation relation, we carry out the mean square convergence analysis and obtain several important theoretical results, including an upper bound on step size that guarantees the mean square convergence, the theoretical steady-state excess mean square error (EMSE), an optimal step size for the… CONTINUE READING
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