A local supervised learning algorithm for multi-layer perceptrons

被引：0

作者：

Vlachos, DS ^{[1
]}

机构：

[1] Hellenic Ctr Marine Res, Anavyssos 19013, Greece

来源：

ICNAAM 2004: INTERNATIONAL CONFERENCE ON NUMERICAL ANALYSIS AND APPLIED MATHEMATICS 2004 | 2004年

关键词：

D O I：

暂无

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The back propagation of error in multi-layer perceptrons when used for supervised training is a non-local algorithm in space, that is it needs the knowledge of the network topology. On the other hand, learning rules in biological systems with many hidden units, seem to be local in both space and time. In this work, a local learning algorithm is proposed which makes no distinction between input, hidden and output layers. Simulation results are presented and compared with other well known training algorithms. (c) 2004 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

引用

页码：452 / 454

页数：3

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