Topology representing networks
Neural Networks
Neural maps and topographic vector quantization
Neural Networks
Comparing Self-Organizing Maps
ICANN 96 Proceedings of the 1996 International Conference on Artificial Neural Networks
A Self-Organizing Network that Can Follow Non-stationary Distributions
ICANN '97 Proceedings of the 7th International Conference on Artificial Neural Networks
A self-organising network that grows when required
Neural Networks - New developments in self-organizing maps
Hand Gesture Recognition Following the Dynamics of a Topology-Preserving Network
FGR '02 Proceedings of the Fifth IEEE International Conference on Automatic Face and Gesture Recognition
Neural maps in remote sensing image analysis
Neural Networks - 2003 Special issue: Neural network analysis of complex scientific data: Astronomy and geosciences
Robust growing neural gas algorithm with application in cluster analysis
Neural Networks - 2004 Special issue: New developments in self-organizing systems
Graph-based normalization and whitening for non-linear data analysis
Neural Networks - 2006 Special issue: Advances in self-organizing maps--WSOM'05
Growing Neural Gas (GNG): A Soft Competitive Learning Method for 2D Hand Modelling
IEICE - Transactions on Information and Systems
Three-dimensional surface reconstruction using meshing growing neural gas (MGNG)
The Visual Computer: International Journal of Computer Graphics
CIARP'06 Proceedings of the 11th Iberoamerican conference on Progress in Pattern Recognition, Image Analysis and Applications
Automatic landmarking of 2d medical shapes using the growing neural gas network
CVBIA'05 Proceedings of the First international conference on Computer Vision for Biomedical Image Applications
Automatic landmark extraction from image data using modified growing neural gas network
IEEE Transactions on Information Technology in Biomedicine
Topology preservation in self-organizing feature maps: exact definition and measurement
IEEE Transactions on Neural Networks
Quantifying the neighborhood preservation of self-organizing feature maps
IEEE Transactions on Neural Networks
`Neural-gas' network for vector quantization and its application to time-series prediction
IEEE Transactions on Neural Networks
Model probability in self-organising maps
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advences in computational intelligence - Volume Part II
Review: A review of novelty detection
Signal Processing
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This paper aims to address the ability of self-organizing neural network models to manage real-time applications. Specifically, we introduce fAGNG (fast Autonomous Growing Neural Gas), a modified learning algorithm for the incremental model Growing Neural Gas (GNG) network. The Growing Neural Gas network with its attributes of growth, flexibility, rapid adaptation, and excellent quality of representation of the input space makes it a suitable model for real time applications. However, under time constraints GNG fails to produce the optimal topological map for any input data set. In contrast to existing algorithms, the proposed fAGNG algorithm introduces multiple neurons per iteration. The number of neurons inserted and input data generated is controlled autonomous and dynamically based on a priory or online learnt model. A detailed study of the topological preservation and quality of representation depending on the neural network parameter selection has been developed to find the best alternatives to represent different linear and non-linear input spaces under time restrictions or specific quality of representation requirements.