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Predictions Tasks with Words and Sequences: Comparing a Novel Recurrent Architecture with the Elman Network

In English

Författare

  • David Gil
  • J Garcia
  • M Cazorla
  • Magnus Johnsson

Summary, in English

The classical connectionist models are not well suited to working with data varying over time. According to this, temporal connectionist models have emerged and constitute a continuously growing research field. In this paper we present a novel supervised recurrent neural network architecture (SARASOM) based on the Associative Self-Organizing Map (A-SOM). The A-SOM is a variant of the Self-Organizing Map (SOM) that develops a representation of its input space as well as learns to associate its activity with an arbitrary number of additional inputs. In this context the A-SOM learns to associate its previous activity with a delay of one iteration. The performance of the SARASOM was evaluated and compared with the Elman network in a number of prediction tasks using sequences of letters (including some experiments with a reduced lexicon of 10 words). The results are very encouraging with SARASOM learning slightly better than the Elman network.

Publiceringsår

2011

Språk

Engelska

Sidor

1207-1213

Publikation/Tidskrift/Serie

[Host publication title missing]

Dokumenttyp

Konferensbidrag

Ämne

  • Medical Biotechnology
  • Computer Vision and Robotics (Autonomous Systems)

Conference name

International Joint Conference on Neural Networks (IJCNN) 2011

Conference date

2011-07-31 - 2011-08-05

Conference place

San Jose, California, United States

Status

Published

Projekt

  • Thinking in Time: Cognition, Communication and Learning

Forskningsgrupp

  • Lund University Cognitive Science (LUCS)

ISBN/ISSN/Övrigt

  • ISSN: 2161-4393
  • ISBN: 978-1-4244-9635-8