The Brain & Neural Networks
Online ISSN : 1883-0455
Print ISSN : 1340-766X
ISSN-L : 1340-766X
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Volume 4, Issue 4
Displaying 1-8 of 8 articles from this issue
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  • Conditions for Convergence of the Normalized LMS Algorithm in Neural Learning
    Kazushi Ikeda, Seiji Miyoshi, Kenji Nakayama
    1997 Volume 4 Issue 4 Pages 151-156
    Published: December 05, 1997
    Released on J-STAGE: December 13, 2010
    DOIhttps://doi.org/10.3902/jnns.4.151
    JOURNAL FREE ACCESS
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    The Perceptron Learning algorithm for linear dichotomy can be regarded as the LMS algorithm which is one of the most popular algorithms for transversal filters. The Normalized LMS (N-LMS) algorithm is one of the improved versions of the LMS algorithm for transversal filters and we apply it to linear dichotomies. In this paper, the proof of the convergence of the N-LMS algorithm for linear dichotomies in a finite number of iterations when the learning coefficient μ is unity, and a sufficient condition of μ for the convergence are given.
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