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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Rights and permissions
Copyright information
© 2007 Springer Science+Business Media, LLC
About this chapter
Cite this chapter
(2007). Nonlinear Adaptive System Identification Based on Wiener Models (Part 3). In: Adaptive Nonlinear System Identification. Signals And Communication Technology. Springer, Boston, MA. https://doi.org/10.1007/978-0-387-68630-1_9
Download citation
DOI: https://doi.org/10.1007/978-0-387-68630-1_9
Publisher Name: Springer, Boston, MA
Print ISBN: 978-0-387-26328-1
Online ISBN: 978-0-387-68630-1
eBook Packages: EngineeringEngineering (R0)