On the rate of convergence in topology preserving neural networks

DOI10.1007/BF00197290zbMath0731.92002OpenAlexW2061749418WikidataQ46073877 ScholiaQ46073877MaRDI QIDQ808996

B. Bavarian, Z.-P. Lo

Publication date: 1991

Published in: Biological Cybernetics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/bf00197290

zbMATH Keywords

feature mapserror measure functionalKohonen learningmexican hutneighborhood interaction functionrate of self- organization to final equilibrium statetopology preserving unsupervised neural network

Mathematics Subject Classification ID

Learning and adaptive systems in artificial intelligence (68T05) Neural networks for/in biological studies, artificial life and related topics (92B20)

Related Items (13)

Self-organization of the batch Kohonen network under quantization effects ⋮ Alternative learning vector quantization ⋮ Study of the Kohonen network with a discrete state space ⋮ A self-organizing cluster process ⋮ The Time-Organized Map Algorithm: Extending the Self-Organizing Map to Spatiotemporal Signals ⋮ Conformality in the self-organization network ⋮ Self-organizing maps: Stationary states, metastability and convergence rate ⋮ Self-organizing maps: Ordering, convergence properties and energy functions ⋮ Kohonen neural networks and genetic classification ⋮ Self-organizing map for symbolic data ⋮ Clustering: a neural network approach ⋮ Extending the Kohonen self-organizing map networks for clustering analysis. ⋮ The effect of sample size on the extended self-organizing map network -- a market segmentation application

Cites Work

• On the stationary state of Kohonen's self-organizing sensory mapping
• A stochastic model of retinotopy: A self organizing process
• Convergence properties of Kohonen's topology conserving maps: Fluctuations, stability, and dimension selection
• Formation of topographic maps and columnar microstructures in nerve fields
• Self-organized formation of topologically correct feature maps
• Analysis of a simple self-organizing process
• Self-organization and associative memory.

This page was built for publication: On the rate of convergence in topology preserving neural networks