Robustness and convergence of adaptive schemes in blind equalization and neural network training

We pursue a time-domain feedback analysis of adaptive schemes with nonlinear update relations. We consider commonly used algorithms in blind equalization and neural network training and study their performance in a purely deterministic framework. The derivation employs insights from system theory an...

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Bibliographic Details
Published in1996 8th European Signal Processing Conference (EUSIPCO 1996) pp. 1 - 4
Main Authors Sayed, Ali H., Rupp, Markus
Format Conference Proceeding
LanguageEnglish
Published IEEE 01.09.1996
Subjects
Algorithm design and analysis
Bit error rate
Convergence
Receivers
Robustness
Signal processing algorithms
Training
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Summary:We pursue a time-domain feedback analysis of adaptive schemes with nonlinear update relations. We consider commonly used algorithms in blind equalization and neural network training and study their performance in a purely deterministic framework. The derivation employs insights from system theory and feedback analysis, and it clarifies the combined effects of the step-size parameters and the nature of the nonlinear functionals on the convergence and robustness performance of the adaptive schemes.
ISBN:8886179839
9788886179836