A sequential dynamic heteroassociative memory for multistep pattern recognition and one-to-many association

IEEE Transactions on Neural Networks
Sylvain Chartier, Mounir Boukadoum

Abstract

Bidirectional associative memories (BAMs) have been widely used for auto and heteroassociative learning. However, few research efforts have addressed the issue of multistep vector pattern recognition. We propose a model that can perform multi step pattern recognition without the need for a special learning algorithm, and with the capacity to learn more than two pattern series in the training set. The model can also learn pattern series of different lengths and, contrarily to previous models, the stimuli can be composed of gray-level images. The paper also shows that by adding an extra autoassociative layer, the model can accomplish one-to-many association, a task that was exclusive to feedforward networks with context units and error backpropagation learning.

References

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Mar 29, 2006·IEEE Transactions on Neural Networks·Sylvain Chartier, Mounir Boukadoum
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Citations

Jul 17, 2009·Neural Networks : the Official Journal of the International Neural Network Society·Sylvain ChartierDominic Langlois
Aug 5, 2008·Neural Networks : the Official Journal of the International Neural Network Society·Sébastien Hélie
Nov 30, 2006·IEEE Transactions on Neural Networks·Janusz A StarzykYue Li
Feb 14, 2008·IEEE Transactions on Neural Networks·B B Nasution, A I Khan
Mar 28, 2007·IEEE Transactions on Neural Networks·Janusz A Starzyk, Haibo He
Jul 15, 2009·IEEE Transactions on Neural Networks·Sylvain ChartierMahmood Amiri
Apr 2, 2009·IEEE Transactions on Neural Networks·Janusz A Starzyk, Haibo He

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