Convergence of a hebbian-type learning algorithm

Qingfu Zhang; Yiu-Wing Leung

doi:10.1109/82.746680

Convergence of a hebbian-type learning algorithm

Qingfu Zhang^*, Yiu-Wing Leung

^*Corresponding author for this work

Department of Computer Science

Research output: Contribution to journal › Journal article › peer-review

7 Citations (Scopus)

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Abstract

A Hebbian-type learning algorithm was proposed in [1] for extracting the minor components of the input signals. In this paper, we demonstrate that some solutions of the averaging differential equation of this algorithm can become unbounded in a finite time. We derive five sufficient conditions to ensure that the solutions of its averaging differential equation are bounded and can be extended to the time interval [0, ∞). Any one of these conditions can guarantee that this algorithm can be used to lind the minor components of the input signals.

Original language	English
Pages (from-to)	1599-1601
Number of pages	3
Journal	IEEE Transactions on Circuits and Systems II: Analog and Digital Signal Processing
Volume	45
Issue number	12
DOIs	https://doi.org/10.1109/82.746680
Publication status	Published - Dec 1998

Scopus Subject Areas

Signal Processing
Electrical and Electronic Engineering

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Convergence of a hebbian-type learning algorithm

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