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Fig. 2 Example of a distribution change not detected by the average quantization error.

Fig. 1 Behavior of local (E{^{prime }}(t)) vs. average quantization error (overline{qe}(t)).

Fig. 7 Average quantization error (overline{qe}(t)) of algorithms across all data streams.

Consequently, we computed the average quantization error (overline{qe}) with (T=2000) for all algorithms and data streams.

The average quantization error (overline{qe}(t)) may be a good overall indicator of the fit of the model.

In short: (overline{qe}(t)) The average quantization error gives an indication of how well the map is currently quantifying the underlying distribution, previously defined in Eq. (6).

We selected a SOM projection with a small mean quantization error.

Using this approximation and the upper bound of the quantization error, the average signal power is approximated as follows: E log h kk [ n ] w k, eg [ n ] 2 ≈ log ρ l 2 − 2 − B N t − 1 ln ( 2 ) + ψ N t ln ( 2 ).

Figure 4 The probability density function of the quantization error under the averaging attack of copies when and using the scaling parameters.

The work also has established the criteria of choosing an optimum SOM size based on results of quantization error, topography error, and average distortion measure during SOM training which have generated the best clustering and preservation of topology.

The averaged copy contains the sum of such attenuated quantization error, hence the total energy of the quantization error in the averaged copy is estimated to be.