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Neural Computing and Applications

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01-07-2015 | Original Article

SARASOM: a supervised architecture based on the recurrent associative SOM

Authors: David Gil, Jose Garcia-Rodriguez, Miguel Cazorla, Magnus Johnsson

Published in: Neural Computing and Applications | Issue 5/2015

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Abstract

We present and evaluate a novel supervised recurrent neural network architecture, the SARASOM, based on the associative self-organizing map. The performance of the SARASOM is evaluated and compared with the Elman network as well as with a hidden Markov model (HMM) in a number of prediction tasks using sequences of letters, including some experiments with a reduced lexicon of 15 words. The results were very encouraging with the SARASOM learning better and performing with better accuracy than both the Elman network and the HMM.

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Title: SARASOM: a supervised architecture based on the recurrent associative SOM
Authors: David Gil
Jose Garcia-Rodriguez
Miguel Cazorla
Magnus Johnsson
Publication date: 01-07-2015
Publisher: Springer London
Published in: Neural Computing and Applications / Issue 5/2015
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI: https://doi.org/10.1007/s00521-014-1785-8

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