A method of resume-training of discontinuous wear state trackers for composing boosting high-accurate ensembles needed to regard statistical data inaccuracies and shifts
Abstract
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-noised-data-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.References
1. Romanuke V.V. 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 / V.V. Romanuke // Problems of tribology. – 2015. – N. 1. – P. 65 – 68.
2. Romanuke V.V. Equally-weighted compositions of Gaussian-noised-data-trained two-layer percep-trons in boosting ensembles for high-accurate discontinuous tracking of wear states regarding statistical data in-accuracies and shifts / V.V. Romanuke // Problems of tribology. – 2015. – N. 2. – P. 53 - 56.
2. Romanuke V.V. Equally-weighted compositions of Gaussian-noised-data-trained two-layer percep-trons in boosting ensembles for high-accurate discontinuous tracking of wear states regarding statistical data in-accuracies and shifts / V.V. Romanuke // Problems of tribology. – 2015. – N. 2. – P. 53 - 56.
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Published
2016-02-12
How to Cite
Романюк, В. (2016). A method of resume-training of discontinuous wear state trackers for composing boosting high-accurate ensembles needed to regard statistical data inaccuracies and shifts. Problems of Tribology, 77(3), 19–22. Retrieved from https://tribology.khnu.km.ua/index.php/ProbTrib/article/view/458
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