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Nonlinear Adaptive System Identification Based on Wiener Models (Part 3)

Part of the Signals And Communication Technology book series (SCT)

In this third part of the book focused on nonlinear adaptive system identification algorithms based on the Wiener model, we discuss some algorithms which are suitable for situations where the environment leads to a non-white, possibly non-Gaussian input signal. We also discuss using other stochasticgradient- based algorithms like the least-mean-fourth (LMF) algorithm for the Wiener model.

Keywords

Wiener Filter Volterra Series Volterra Model Volterra System Autocorrelation Matrix 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2007

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