Flexible architecture of self organizing maps for changing environments

Rodrigo Salas, Héctor Allende, Sebastián Moreno, Carolina Saavedra

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


Catastrophic Interference is a well known problem of Artificial Neural Networks (ANN) learning algorithms where the ANN forget useful knowledge while learning from new data. Furthermore the structure of most neural models must be chosen in advance. In this paper we introduce a hybrid algorithm called Flexible Architecture of Self Organizing Maps (FASOM) that overcomes the Catastrophic Interference and preserves the topology of Clustered data in changing en- 7 vironments. The model consists in K receptive fields of self organizing maps. Each Receptive Field projects high-dimensional data of the input space onto a neuron position in a low-dimensional output space grid by dynamically adapting its structure to a specific region of the input space. Furthermore the FASOM model automatically finds the number of maps and prototypes needed to successfully adapt to the data. The model has the capability of both growing its structure when novel clusters appears and gradually forgets when the data volume is reduced in its receptive fields. Finally we show the capabilities of our model with experimental results using synthetic sequential data sets and real world data.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis and Applications - 10th Iberoamerican Congress on Pattern Recognition, CIARP 2005, Proceedings
Number of pages12
StatePublished - 2005
Externally publishedYes
Event10th Iberoamerican Congress on Pattern Recognition, CIARP 2005 - Havana, Cuba
Duration: 15 Nov 200518 Nov 2005

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3773 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference10th Iberoamerican Congress on Pattern Recognition, CIARP 2005


  • Artificial Neural Networks
  • Catastrophic Interference
  • Pattern Recognition
  • Self Organizing Maps


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