A filter based neuron model for adaptive incremental learning of self-organizing maps

Authors:
Mauro Tucci;Marco Raugi
Affiliations:
Department of Electric Systems and Automation, University of Pisa, Pisa, Italy;Department of Electric Systems and Automation, University of Pisa, Pisa, Italy
Venue:
Neurocomputing
Year:
2011

Citing 8
Cited 0

Self-Organizing Maps

Self-Organizing Maps
Extensions of vector quantization for incremental clustering

Pattern Recognition
TASOM: a new time adaptive self-organizing map

IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Self-organizing feature maps with self-adjusting learning parameters

IEEE Transactions on Neural Networks
Predictive self-organizing map for vector quantization of migratory signals and its application to mobile communications

IEEE Transactions on Neural Networks
The parameterless self-organizing map algorithm

IEEE Transactions on Neural Networks
Rival-Model Penalized Self-Organizing Map

IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction

IEEE Transactions on Neural Networks

Quantified Score

Hi-index	0.01

Visualization

Abstract

In this work a learning algorithm is proposed for the formation of topology preserving maps. In the proposed algorithm the weights are updated incrementally using a higher-order difference equation, which implements a low-pass digital filter. It is shown that by suitably choosing the filter the learning process can adaptively follow a specific dynamic. Numerical results, for time-varying and static distributions, show the potential of the proposed method for unsupervised learning.