Learning by a neural net in a noisy environment—the pseudo-inverse solution revisited

W.A. van Leeuwen and B. Wemmenhove

Physica A: Statistical Mechanics and its Applications, 2003, vol. 319, issue C, 616-632

Abstract: A recurrent neural net is studied that learns a set of patterns {ξμ} in the presence of noise. The learning rule is of a Hebbian type. It is well-known that, if noise is absent during the learning process, the resulting final values of the weights wij correspond to what is usually referred to as the pseudo-inverse solution of the fixed point equation associated with the learning rule. In the limit of vanishing noise, the expressions derived in this article for the expectation value of the weights do not converge to the usual pseudo-inverse solution, in contrast to what one might expect. Since biological systems in general are noisy, the usual pseudo-inverse solution is less realistic, in principle, than the solution found in this article.

Date: 2003
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437102015182
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:319:y:2003:i:c:p:616-632

DOI: 10.1016/S0378-4371(02)01518-2

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().