An Approach to Collaboration of Growing Self-Organizing Maps and Adaptive Resonance Theory Maps

Authors:
Masaru Takanashi;Hiroyuki Torikai;Toshimichi Saito
Affiliations:
-;-;-
Venue:
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Year:
2007

Citing 3
Cited 3

Growing cell structures—a self-organizing network for unsupervised and supervised learning

Neural Networks
ARTMAP neural networks for information fusion and data mining: map production and target recognition methodologies

Neural Networks
ART-based parallel learning of growing SOMs and its application to TSP

ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I

Parallel ant colony optimizers with local and global ants

IJCNN'09 Proceedings of the 2009 international joint conference on Neural Networks
Parallel ant colony optimizer based on adaptive resonance theory maps

ICONIP'08 Proceedings of the 15th international conference on Advances in neuro-information processing - Volume Part I
Letters: Growing fuzzy topology adaptive resonance theory models with a push-pull learning algorithm

Neurocomputing

Quantified Score

Hi-index	0.00

Visualization

Abstract

Collaboration of growing self-organizing maps (GSOM) and adaptive resonance theory maps (ART) is considered through traveling sales-person problems (TSP).The ART is used to parallelize the GSOM: it divides the input space of city positions into subspaces automatically. One GSOM is allocated to each subspace and grows following the input data. After all the GSOMs grow sufficiently they are connected and we obtain a tour. Basic experimental results suggest that we can find semi-optimal solution much faster than serial methods.