Neural Networks
Self-Organizing Maps
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Neural and Adaptive Systems: Fundamentals through Simulations with CD-ROM
Recursive self-organizing maps
Neural Networks - New developments in self-organizing maps
Recursive self-organizing network models
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Dynamics and Topographic Organization of Recursive Self-Organizing Maps
Neural Computation
Neurocomputing
Gamma-filter self-organizing neural networks for time series analysis
WSOM'11 Proceedings of the 8th international conference on Advances in self-organizing maps
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In this paper, we introduce the Gamma SOM model for temporal sequence processing. The standard SOM is merged with a new context descriptor based on a short term memory structure called Gamma memory. The proposed model allows increasing depth without losing resolution, by adding more contexts. When using a single stage of the Gamma filter, the Merge SOM model is recovered. The temporal quantization error is used as a performance measure. Simulation results are presented using two data sets: Mackey-Glass time series, and Bicup 2006 challenge time series. Gamma SOM surpassed Merge SOM in terms of lower temporal quantization error in these data sets.