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A Novel Family of Adaptive Filtering Algorithms Based on the Logarithmic Cost

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Fig. 1: General system identification configuration.
Fig. 2: Here, we plot stochastic cost functions to illustrate decreased steepness of the least squares algorithms in the logarithmic error cost framework for small error amounts.
TABLE I: h G (e t ) and h U (e t ) corresponding to the stochastic costs e 2 t and |e t |, where σ e 2 = E[e 2 t ] and λ = 2ασ 1 2 e = ακ
Fig. 3: Dependence of the steady-state MSD on the step size µ for the LMLS and LLAD algorithms.
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