By L. Morales
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Extra resources for Adaptive Filtering
5) i That is to say, getting is equivalent to getting the MSE. A first-order random-walk model is widely used to get the tracking performance in nonstationary environments , , which assumes that w o ,i appearing in (3) undergoes random variations of the form w o ,i 1 w o , i qi , (6) where qi is M 1 column vector and denotes some random perturbation. , zero-mean, with M M covariance matrix E qiqH Q , which is independent of the regressor sequences u i and weight-error i i.
2 Steady-State Performance Analyses of Adaptive Filters Bin Lin and Rongxi He College of Information Science and Technology, Dalian Maritime University, Dalian, China 1. Introduction Adaptive filters have become a vital part of many modern communication and control systems, which can be used in system identification, adaptive equalization, echo cancellation, beamforming, and so on [l]. The least mean squares (LMS) algorithm, which is the most popular adaptive filtering algorithm, has enjoyed enormous popularity due to its simplicity and robustness  .
The least mean squares (LMS) algorithm, which is the most popular adaptive filtering algorithm, has enjoyed enormous popularity due to its simplicity and robustness  . Over the years several variants of LMS have been proposed to overcome some limitations of LMS algorithm by modifying the error estimation function from linearity to nonlinearity. Sign-error LMS algorithm is presented by its computational simplicity , least-mean fourth (LMF) algorithm is proposed for applications in which the plant noise has a probability density function with short tail , and the LMMN algorithm achieves a better steady state performance than the LMS algorithm and better stability properties than the LMF algorithm by adjusting its mixing parameter , .