Monthly
288 pp. per issue
6 x 9, illustrated
ISSN
0899-7667
E-ISSN
1530-888X
2014 Impact factor:
2.21

Neural Computation

September 1995, Vol. 7, No. 5, Pages 923-930
(doi: 10.1162/neco.1995.7.5.923)
© 1995 Massachusetts Institute of Technology
Learning the Initial State of a Second-Order Recurrent Neural Network during Regular-Language Inference
Article PDF (396.42 KB)
Abstract

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.