LEARNING THE INITIAL-STATE OF A 2ND-ORDER RECURRENT NEURAL-NETWORK DURING REGULAR-LANGUAGE INFERENCE

被引：24

作者：

FORCADA, ML

CARRASCO, RC

机构：

来源：

NEURAL COMPUTATION | 1995年 / 7卷 / 05期

关键词：

D O I：

10.1162/neco.1995.7.5.923

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Recent work has shown that second-order recurrent neural networks (2ORNNs) may be used to infer regular languages. This paper presents a modified version of the real-time recurrent learning (RTRL) algorithm used to train 2ORNNs, that learns the initial state in addition to the weights. The results of this modification, which adds extra flexibility at a negligible cost in time complexity, suggest that it may be used to improve the learning of regular languages when the size of the network is small.

引用

页码：923 / 930

页数：8

共 7 条

[1] GILES CL, 1992, ADV NEUR IN, V4, P317
[2] LEARNING AND EXTRACTING FINITE STATE AUTOMATA WITH 2ND-ORDER RECURRENT NEURAL NETWORKS
GILES, CL
MILLER, CB
CHEN, D
CHEN, HH
SUN, GZ
LEE, YC
[J]. NEURAL COMPUTATION, 1992, 4 (03) : 393 - 405
[3] SIEGELMANN HT, 1992, INFORMATION PROCESSI, V1, P329
[4] TOMITA M, 1982, 4TH P ANN COGN SCI C, P105
[5] INDUCTION OF FINITE-STATE LANGUAGES USING 2ND-ORDER RECURRENT NETWORKS
WATROUS, RL
KUHN, GM
[J]. NEURAL COMPUTATION, 1992, 4 (03) : 406 - 414
[6] WATROUS RL, 1992, ADV NEURAL INFORMATI, V4, P306
[7] A Learning Algorithm for Continually Running Fully Recurrent Neural Networks
Williams, Ronald J.
Zipser, David
[J]. NEURAL COMPUTATION, 1989, 1 (02) : 270 - 280

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