On the convergence behavior of the LMS and the normalized LMS algorithms
From MaRDI portal
Publication:4276657
DOI10.1109/78.236504zbMath0800.94093OpenAlexW2030319782MaRDI QIDQ4276657
Publication date: 7 February 1994
Published in: IEEE Transactions on Signal Processing (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1109/78.236504
Filtering in stochastic control theory (93E11) Signal theory (characterization, reconstruction, filtering, etc.) (94A12)
Related Items (18)
A new approach for analyzing the limiting behavior of the normalized LMS algorithm under weak assumptions ⋮ A new data-reusing algorithm based on minimum norm and minimum disturbance principles ⋮ Widely linear complex-valued least mean M-estimate algorithms: design and performance analysis ⋮ Online sparse identification for regression models ⋮ Mean-square performance of the modified filtered-x affine projection algorithm ⋮ Stability and convergence analysis of direct adaptive inverse control ⋮ A novel convergence accelerator for the LMS adaptive filter ⋮ Statistical tracking behavior of affine projection algorithm for unity step size ⋮ Statistical convergence behavior of affine projection algorithms ⋮ Robust adaptive beam-forming optimization method based on diagonal-loading and MSE criterion ⋮ New efficient adaptive fast transversal filtering (FTF)‐type algorithms for mono and stereophonic acoustic echo cancelation ⋮ Extension of LMS stability condition over a wide set of signals ⋮ Parameter estimation for time varying nonlinear circuit from state analysis and simulation ⋮ Scalable estimation strategies based on stochastic approximations: classical results and new insights ⋮ A fast convergence normalized least-mean-square type algorithm for adaptive filtering ⋮ Design of normalized fractional adaptive algorithms for parameter estimation of control autoregressive autoregressive systems ⋮ Convergence analysis of adaptive DS-CDMA receivers in multi-path channels ⋮ A bias-compensated fractional order normalized least mean square algorithm with noisy inputs
This page was built for publication: On the convergence behavior of the LMS and the normalized LMS algorithms
