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Unsupervised Learning with Growing Neural Gas (GNG) Neural Network


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Unsupervised Learning with Growing Neural Gas (GNG) Neural Network



Learns data clusters and their topology in n-dimensional space by using the Growing Neural Gas net.

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The Growing Neural Gas (GNG) Neural Network belongs to the class of Topology Representing Networks (TRN's). It can learn supervised and unsupervised. Here, the on-line, unsupervised learning mode is implemented and demonstrated. It's learning method employs a combination of modified Kohonen learning to adjust the neuron's positions, with a Competitive Hebbian Learning (CHL) for its connections. For details please consult ref. [1]. In order to make the main script (gng_lax.m) functional, you must first select and generate a manifold (data) using the corresponding data generator. For a nice report on the family of competitive learning methods please consult ref. [2].

[1] Fritzke B. "A Growing Neural Gas Network Learns Topologies", Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995.

[2] Fritzke B. "Some Competitive Learning Methods", 1997 available at:


Unsupervised Learning With Dynamic Cell Structures (Dcs) Neural Network inspired this file.

MATLAB release MATLAB 7.13 (R2011b)
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Comments and Ratings (7)
29 Aug 2014 Parivash Ashrafi

hello. Nobody is here to answer my question plz...

25 Aug 2014 Parivash Ashrafi

I get an error when I run it loading local_uniform_2d.mat because it is a 2*400 size matrix and in line 74 in the second loop the Index exceeds matrix dimensions. because it wants to go for Data(:,400:800)
which does not exist! What is the problem. Please help. Thanks

31 Jul 2014 Pedro Ribeiro  
22 Apr 2014 Ilias Konsoulas

@Pedro and @Patricia: Thnks a lot for your kind words.

19 Apr 2014 Pedro Ribeiro

The outstanding quality of this implementation of the GNG algorithm is truly remarkable. Many thanks are in order to congratulate Mr. Ilias Konsoulas for his astonishing work.

19 Apr 2014 Patricia Souza  
19 Apr 2014 Patricia Souza

I would like to congratulate you for the excellent work done was of great value to my studies.

Thank you.

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