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**Self-organizing maps: Stationary states, metastability and convergence rate.**
*(English)*
Zbl 0747.92005

Summary: We investigate the effect of various types of neighborhood function on the convergence rates and the presence or absence of metastable stationary states of T. Kohonen’s [see “Self-organization and associative memory”, 2nd ed. (1988; Zbl 0659.68100)] self-organizing feature map algorithm in one dimension. We demonstrate that the time necessary to form a topographic representation of the unit interval \([0,1]\) may vary over several orders of magnitude depending on the range and also the shape of the neighborhood function, by which the weight changes of the neurons in the neighborhood of the winning neuron are scaled.

We will prove that for neighborhood functions which are convex on an interval given by the length of the Kohonen chain there exist no metastable states. For all other neighborhood functions, metastable states are present and may trap the algorithm during the learning process. For the widely-used Gaussian function there exists a threshold for the width above which metastable states cannot exist. Due to the presence or absence of metastable states, convergence time is very sensitive to slight changes in the shape of the neighborhood function. Fastest convergence is achieved using neighborhood functions which are “convex” over a large range around the winner neuron and yet have large differences in value at neighboring neurons.

We will prove that for neighborhood functions which are convex on an interval given by the length of the Kohonen chain there exist no metastable states. For all other neighborhood functions, metastable states are present and may trap the algorithm during the learning process. For the widely-used Gaussian function there exists a threshold for the width above which metastable states cannot exist. Due to the presence or absence of metastable states, convergence time is very sensitive to slight changes in the shape of the neighborhood function. Fastest convergence is achieved using neighborhood functions which are “convex” over a large range around the winner neuron and yet have large differences in value at neighboring neurons.

### MSC:

92B20 | Neural networks for/in biological studies, artificial life and related topics |

68T05 | Learning and adaptive systems in artificial intelligence |

### Keywords:

SOFM algorithm; convergence rates; metastable stationary states; self- organizing feature map algorithm; topographic representation of the unit interval; neighborhood functions; Gaussian function; threshold### Citations:

Zbl 0659.68100
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\textit{E. Erwin} et al., Biol. Cybern. 67, No. 1, 35--45 (1992; Zbl 0747.92005)

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### References:

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