Abstract
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 [1]–[10], the frequency-domain (or transform-domain) LMS algorithm [11]–[13], the normalized LMS algorithm [14], and the LMS-Newton algorithm [15]–[16]. Several algorithms that are related to the main algorithms presented in this chapter are also briefly discussed.
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Diniz, P.S.R. (1997). LMS-Based Algorithms. In: Adaptive Filtering. The Springer International Series in Engineering and Computer Science, vol 399. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8660-3_4
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