Titlebook: Adaptive Filtering; Algorithms and Pract Paulo S. R. Diniz Textbook 20134th edition Springer Science+Business Media New York 2013 Adaptive

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Luis M. Camarinha-Matos,Hamideh AfsarmaneshThis chapter briefly describes how to deal with complex signals in adaptive-filtering context in a simple manner; for further details the reader is encouraged to refer to [1–4].

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Prologue

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Luís Seabra Lopes,Luís M. Camarinha-Matos the concepts that are directly relevant to adaptive filtering. The properties of thecorrelation matrix of the input signal vector are investigated in some detail, since they play a key role in the statistical analysis of the adaptive-filtering algorithms.

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R. Roshardt,C. Uhrhan,T. Waefler,S. Weikective of the alternative LMS-based algorithms is either to reduce computational complexity or convergence time. In this chapter, several LMS-based algorithms are presented and analyzed, namely, the quantized-error algorithms [1–11], the frequency-domain (or transform-domain) LMS algorithm [12–14],

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R. Roshardt,C. Uhrhan,T. Waefler,S. Weikient. These characteristics are easily observable in stationary environments. In general fast-converging algorithms tend to be very dynamic, a feature not necessarily advantageous after convergence in a stationary environment. In this chapter, an alternative formulation to govern the updating of the

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https://doi.org/10.1007/978-0-387-35065-3alization are very attractive because they allow modular implementation and require a reduced number of arithmetic operations (of order .) [1–7]. As a consequence, the lattice recursive least-squares (LRLS) algorithms are considered fast implementations of the RLS problem.

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