There are a number of algorithms for adaptive filters which are derived from the conventional LMS algorithm discussed in the previous chapter. The objective of the alternative LMS-based algorithms is either to reduce computational complexity or convergence time. In this chapter, four LMS-based algorithms are presented and analyzed, namely, the quantized-error algorithms –, the frequency-domain (or transform-domain) LMS algorithm –, the normalized LMS algorithm , and the LMS-Newton algorithm –. Several algorithms that are related to the main algorithms presented in this chapter are also briefly discussed.
KeywordsInput Signal Adaptive Filter Convergence Factor Repeat Problem Eigenvalue Spread
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