Abstract
This paper analyses the statistical convergence properties of the modified NLMS rules which were formulated in an attempt to produce more robust and faster converging training algorithms. However, the statistical analysis described in this paper leads us to the conjecture that the standard NLMS rule is the only unconditionally stable modified NLMS training algorithm, and that the optimal value of the learning rate and region of convergence for the modified NLMS rules is generally less than for the standard NLMS rule.
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© 1997 Springer Science+Business Media New York
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Aved’yan, E.D., Brown, M., Harris, C.J. (1997). A Statistical Analysis of the Modified NLMS Rules. In: Ellacott, S.W., Mason, J.C., Anderson, I.J. (eds) Mathematics of Neural Networks. Operations Research/Computer Science Interfaces Series, vol 8. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-6099-9_10
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DOI: https://doi.org/10.1007/978-1-4615-6099-9_10
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