EQUALLY - WEIGHTED COMPOSITIONS OF GAUSSIAN - NOISED - DATA - TRAINED - Romanuke, V.V. (Problems of Tribology, 2015)
Equally - weighted compositions of Gaussian - noised-data - trained two - layer perceptrons are studied in order to track metal wear states more accurately at the highest level of statistical data inaccuracies and shifts (noise). The noise range is modeled through four magnitudes characterizing ultimate jitters and shifts in wear influencing factors. Accuracy and vari-ance gains of equally - weighted compositions seem to be increasing when noise intensities become lower. When boosting ensembles are composed from ordinary classifiers, high-accurate tracking fails. Only composing ensembles from a lot of the best optimized perceptrons, the accuracy improves by 1,5 % for the averaged tracking error rate and by 7,7 % for the tracking error rate at noise maximum. Here, the boosting appears to have its limit. But ensembles of equally-weighted compositions of perceptrons perform even better than ensembles of perceptrons weighted after training. And for ensuring high-accurate dis-continuous tracking of wear states, we just need perceptrons trained by quite different backpropagation methods