DocumentCode :
3861604
Title :
Robust stability analysis of adaptation algorithms for single perceptron
Author :
S. Hui;S.H. Zak
Author_Institution :
Dept. of Math. Sci., San Diego State Univ., CA, USA
Volume :
2
Issue :
2
fYear :
1991
Firstpage :
325
Lastpage :
328
Abstract :
The problem of robust stability and convergence of learning parameters of adaptation algorithms in a noisy environment for the single preceptron is addressed. The case in which the same input pattern is presented in the adaptation cycle is analyzed. The algorithm proposed is of the Widrow-Hoff type. It is concluded that this algorithm is robust. However, the weight vectors do not necessarily converge in the presence of measurement noise. A modified version of this algorithm in which the reduction factors are allowed to vary with time is proposed, and it is shown that this algorithm is robust and that the weight vectors converge in the presence of bounded noise. Only deterministic-type arguments are used in the analysis. An ultimate bound on the error in terms of a convex combination of the initial error and the bound on the noise is obtained.
Keywords :
"Robust stability","Algorithm design and analysis","Neural networks","Gaussian processes","Convergence","Working environment noise","Pattern analysis","Layout"
Journal_Title :
IEEE Transactions on Neural Networks
Publisher :
ieee
ISSN :
1045-9227
Type :
jour
DOI :
10.1109/72.80346
Filename :
80346
Link To Document :
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