By L. Morales
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194-195, April, 1962.  V. H. Nascimento and J. C. M. Bermudez, “Probability of divergence for the least-mean fourth (LMF) algorithm,” IEEE Trans. Signal Processing, vol. 54, pp. , 2006.  J. C. M. Bermudez and V. H. Nascimento, “A mean-square stability analysis of the least mean fourth adaptive algorithm,” IEEE Trans. Signal Processing, vol. 55, pp. , 2007.  A. Zerguine, "Convergence and steady-state analysis of the normalized least mean fourth algorithm," Digital Signal Processing, vol.
Meng obtained a general expression for the steady-state MSE for adaptive filters with error 20 Adaptive Filtering nonlinearities . However, this expression is only applicable for the cases with the realvalued data and small step-size. Also using TSE, our previous works have obtained some analytical expressions of the steady-state performance for some adaptive algorithms , , , . Using the Price’s theory, T. Y. Al-Naffouri and A. H. Sayed obtained the steady-state performance as the fixed-point of a nonlinear function in EMSE , .
1 Fig. 2 Table 2. 3 Tracking ability comparison with LMS algorithm Consider the real-valued cases with g u i 1 . p 1 2 p 3 !! p 1 2 , 2 p 3 !! p:even , p:odd . (68) in Gaussian noise environments, and LMS 2p 1 min , LMP 3 min (69) in uniformly distributed noise environments, respectively. Under different parameter p LMS LMP min (dB) in Gaussian noise (from 2 to 6), Fig. 12 shows two curves for the ratio of min environments and uniformly distributed noise environments.