Forecasting Models of Tool use in Different Intervals of Time
In the paper the forecasting models of tool use in different intervals of time were presented. The models were worked out by the use of hybrid neural networks in the form of: linear neural network (L) - multi-layer networks with error backpropagation (MLP), L network - Radial Basis Function network (RBF), MLP network - RBF network and L network - MLP network - RBF network. The comparison of these models was executed. The effectiveness of forecasting of tool use in different time intervals is the measure of model evaluation. These models are used at the design stage of manufacturing process with the aim to plan production and prevent standstill due to lack of tools, and special tools in particular. The created models were tested on real data from an enterprise. (original abstract)
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