A LEARNING ALGORITHM TO TEACH SPATIOTEMPORAL PATTERNS TO RECURRENT NEURAL NETWORKS

被引：37

作者：

SATO, M

机构：

[1] ATR Auditory and Visual Perception Research Laboratories, Sanpeidani Inuidani, Kyoto, 619-02, Seika-cho, Soraku-gun

来源：

BIOLOGICAL CYBERNETICS | 1990年 / 62卷 / 03期

关键词：

D O I：

10.1007/BF00198101

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

A new supervised learning algorithm is proposed. It teaches spatiotemporal patterns to the recurrent neural network with arbitrary feedback connections. In this method the network with fixed connection weights is run for a given period of time under a given external input and initial condition. Then the weights are changed so that the total error from the time dependent teacher signal in this period is maximally decreased. This algorithm is equivalent to the back propagation method for the recurrent network if the discrete time prescription is adopted. However, continuous time formalism seems suited for temporal processing application. © 1990 Springer-Verlag.

引用

页码：259 / 263

页数：5

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