Problems of Tribology = Проблеми трибології
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Документ A method of resume-training of discontinuous wear state trackers for composing boosting high-accurate ensembles needed to regard statistical data inaccuracies and shifts(Хмельницький національний університет, 2015) Romanuke, V.V.; Романюк, В.В.For tracking metal wear states at bad statistical data inaccuracies and shifts, there is a method of resume-training of discontinuous wear state trackers for boosting them within high-accurate ensembles. These trackers are Gaussian-noiseddata- trained two-layer perceptrons. An ordinary tracker is selected and, if its performance is satisfactory, it is resumed-trained cyclically. Number of additional passes of training sets is limited. The resume-training procedure wholly can be cycled.Документ Accuracy improvement in wear state discontinuous tracking model regarding statistical data inaccuracies and shifts with boosting miniensemble of two-layer perceptrons(Khmelnitskiy National University, 2014) Romanuke, V.V.; Романюк, В.В.There is presented a method of improving accuracy in tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is presumed to be wholly sampled into those factors. The tracker is a static model based on boosting mini-ensemble of three twolayer perceptrons with nonlinear transfer functions. It regards statistical data inaccuracies and shifts. For making the ensemble, the AdaBoost technique is used. A distinction of the presented method of boosting from the AdaBoost is in the rule for finding the decreasing coefficient in order to re-distribute weights over training samples. Another one is that the ensemble is aggregated at once. The averaged gain of the boosting mini-ensemble in tracking 24 wear states with 16 influencing factors exceeds 50 %. The wear state tracking model is going to be perfected on optimizing two parameters of the training set and the naive rule for finding the decreasing coefficient before re-distributing training samples’ weights.Документ Equally - weighted compositions of gaussian - noised - data – trained two - layer perceptrons in boosting ensembles for high - accurate discontinuous tracking of wear states regarding statistical data inaccuracies and shifts(Khmelnitskiy National University, 2015) Romanuke, V.V.; Романюк, В.В.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 variance 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 discontinuous tracking of wear states, we just need perceptrons trained by quite different backpropagation methods.Документ Optimizing parameters of the two-layer perceptrons’ boosting ensemble training for accuracy improvement in wear state discontinuous tracking model regarding statistical data inaccuracies and shifts(Khmelnitskiy National University, 2015) Romanuke, V.V.; Романюк, В.В.There is a trial of optimization for improving accuracy in tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is presumed to be wholly sampled into those factors. The tracker is a static model based on boosting ensemble of two-layer perceptrons with nonlinear transfer functions. It successfully regards statistical data inaccuracies and shifts in a problem of tracking 24 wear states featured with 16 wear influencing factors. Having increased number of classifiers within the ensemble up to 30, the averaged gain with the optimized ensemble is about 56 % in respect of the best ensemble of three classifiers. Similarly, variance of tracking error rate over 24 wear states is about 53 % lower. Nearly the same results are registered when the ensemble is composed without training, but just setting every classifier’s weight to one thirtieth. To get the perfected accuracy more, such equally-weighted compositions shall be investigated in the sequel.Документ Дискретна модель відслідковування стану зносу на основі двошарового персептрону з нелінійними передавальними функціями, що навчається на розширеній генеральній сукупності з урахуван- ням похибок і зсувів у статистичних даних.(Хмельницький національний університет, 2014) Романюк, В.В.; Romanuke, V.V.There is presented a framework for tracking metal tool wear states discontinuously, when the states’ finite set has been statistically tied to the set of representative wear influencing factors. Range of wear states is presumed to be wholly sampled into those factors. The tracker is two-layer perceptron with nonlinear transfer functions. It is a static model, unlike evolutionary dynamic models of forecasting wear. Its identification starts with forming the initial finite general totality containing correspondence between influencing factors and each known wear state. Two-layer perceptron is then trained on an extended general totality, whose elements are sum of pure representatives and normal variates’ values in two terms. The first term models jitter inaccuracies and omissions in statistical data or measurements. The second term models possible shifts of wear influencing factors’ values in every state. The identification final stage is the input of two-layer perceptron is re-fed with the pure representatives for making sure that they have not been disassociated from the initially given wear states. It is said also about liable and easy realizability of the tracking model. When range of wear states embraces all practiced wears, the presented two-layer perceptron tracker will control metal tool object wear states with minimized error, ensuring negligibility of underuse or overuse of materials.