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Self-organization of a one-dimensional Kohonen network with quantized weights and inputs

โœ Scribed by Patrick Thiran; Martin Hasler

Publisher
Elsevier Science
Year
1994
Tongue
English
Weight
998 KB
Volume
7
Category
Article
ISSN
0893-6080

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โœฆ Synopsis

If a self-organizing neural network has to be implemented on a digital (or mixed analog and digital) circuit realization, with on-chip learning, all the input signals and the weight values have to be quantized. It is therefore crucial to study whether this quantization does not annihilate the self-organization property of the weights. This paper provides necessary and sufficient conditions on the parameters of a one-dimensional network, which ensure the organization of the weights for any one-dimensional input probability distribution. These results are rigorously proved using the Markovian formulation of Kohonen's algorithm.

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