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. . 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. .
 Fritzke B. "A Growing Neural Gas Network Learns Topologies", Advances in Neural Information Processing Systems 7, MIT Press, Cambridge MA, 1995.
 Fritzke B. "Some Competitive Learning Methods", 1997 available at: https://pdfs.semanticscholar.org/7f13/a0c932e32eb0dbe009dc86badfe8bed31e66.pdf
Ilias Konsoulas (2022). Unsupervised Learning with Growing Neural Gas (GNG) Neural Network (https://www.mathworks.com/matlabcentral/fileexchange/43665-unsupervised-learning-with-growing-neural-gas-gng-neural-network), MATLAB Central File Exchange. Retrieved .
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