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(b) Sample eigenvalue distributions with very low smoothing factor resulting from two population eigenvalue clusters.
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In constructing the SARIMA-GRNN model, the simulation accuracy of the GRNN model was determined by using the smoothing factor δ.After exploring various smoothing factors from 0.1 to 1.0, we found when the smooth factor was 0.1, the hybrid model has lowest MSE, MAE and MAPE.
SF is the smoothing factor.
where α is a smoothing factor.
where ρ denotes the smoothing factor.
The smoothing factor 0<α ol<1 is application specific.
The smoothing factor β was set to 1 - μ.
where the smoothing factor is α p =0.2.
It can be recursively estimated using the single-pole low-pass filter as η ̂ m n = λ η ̂ m n − 1 + 1 − λ b m 2 n, where λ is a smoothing factor.
This means that, unless a large smoothing factor is used, the target earthquake locations may very likely fall into "empty" boxes when the region in question has low to moderate seismicity.
Low smoothing was applied locally to correct small topological irregularities.
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