Performance analysis of LMS adaptive prediction filters
Article Abstract:
Linear prediction is a way to estimate the value of an input process when measured data is not available. The method is based on measurements of the same process at a time which is not the time of interest. Adaptive linear prediction filters provide useful signal processing gains in a wide variety of applications. Experiments to delineate the conditions required to implement real-time adaptive prediction filters so that they supply nearly optimum performance show that a function of the input signal-to-noise ratio results in a nonlinear degradation in the signal processing gain. Tests show that parameter estimators based on adaptive filter output often are better than estimators based adaptive filter weights.
Publication Name: Proceedings of the IEEE
Subject: Electronics
ISSN: 0018-9219
Year: 1990
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Multiplication-free adaptive digital filters
Article Abstract:
New multiplication-free adaptive digital filtering algorithms are proposed that are simple to realize. The algorithms enable the updating of delta modulation digital weights with LMS and SIGN algorithms obviating multiplication operations. The SIGN algorithm uses simple up-down counting operations and converges well, though slower than the LMS algorithm.
Publication Name: Proceedings of the IEEE
Subject: Electronics
ISSN: 0018-9219
Year: 1988
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Two observations regarding first-quadrant causal bibo-stable digital filters
Article Abstract:
First-quadrant causal BIBO-stable digital filters are examined in the light of work by Rudin and Fejer. An expanded account of their work is provided, along with an appendix that restates two essential properties of n-variable polynomials.
Publication Name: Proceedings of the IEEE
Subject: Electronics
ISSN: 0018-9219
Year: 1990
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