In this chapter, we present the calibrated least mean-square (LMS) algorithm developed by Widrow and Hoff in 1960. This algorithm is a member of stochastic gradient algorithms, and because of its robustness and low computational complexity, it has been used in a wide spectrum of applications.
The LMS algorithm has the following most important properties:
1. It can be used to solve the Wiener–Hopf equation without finding matrix inversion. Furthermore, it does not require the availability of the autocorrelation matrix of the filter input and the cross-correlation between the filter input and its desired signal.
2. Its form is simple as well as its implementation, yet it is capable of ...
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