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Nonlinear Adaptive Filtering

  • Paulo Sergio Ramirez
Part of the The Kluwer International Series in Engineering and Computer Science book series (SECS, volume 694)

Abstract

The classic adaptive filtering algorithms, such as those discussed in the remain­ing chapters of this book, consist of adapting the coefficients of linear filters in real time. These algorithms have applications in a number of situations where the signals measured in the environment can be well modeled as Gaussian noises applied to linear systems, and their combinations are of additive type. In digital communication systems, most of the classical approaches model the major impairment affecting the transmission with a linear model. For example, channel noise is considered additive Gaussian noise, intersymbol and co-channel interferences are also considered of additive type, and channel models are as­sumed to be linear frequency selective filters. While these models are accurate, there is nothing wrong with the use of linear adaptive filters 1 to remedy these impairments. However, the current demand for higher-speed communications leads to the exploration of the channel resources beyond the range their models can be considered linear. For example, when the channel is the pair of wires of the telephone system, it is widely accepted that linear models are not valid for data transmission above 4.8 kb/s. Signal companding, amplifier saturation, multiplicative interaction between Gaussian signals, and nonlinear filtering of Gaussian signals are typical phenomena occurring in communication systems that cannot be well modeled with linear adaptive systems.

Keywords

Radial Basis Function Radial Basis Function Network Adaptive Filter Volterra Series Convergence Factor 
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 New York 2002

Authors and Affiliations

  • Paulo Sergio Ramirez
    • 1
  1. 1.Federal University of Rio de JaneiroRio de JaneiroBrazil

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