ACCURACY IMPROVEMENT IN WEAR STATE DISCONTINUOUS TRACKING MODEL REGARDING STATISTICAL DATA INACCURACIES AND SHIFTS WITH BOOSTING MINI-ENSEMBLE OF TWO-LAYER PERCEPTRONS - Romanuke, V.V. (Problems of Tribology, 2015)
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 pre-sumed to be wholly sampled into those factors. The tracker is a static model based on boosting mini-ensemble of three two-layer perceptrons with nonlinear transfer functions. It regards statistical data inaccuracies and shifts. For making the ensem-ble, 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.