Sensor errors prediction using neural networks
Anatoliy Sachenko, Volodymyr Kochan, Volodymyr Turchenko, Vladimir A. Golovko, J. Savitsky, A. Dunets, Theodore Laopoulos · 2000
The features of neural networks used for increasing the accuracy of physical quantity measurement are considered by prediction of sensor drift. The technique of data volume increasing for predicting neural network training is offered at the expense of various data types replacement for neural network training and at the expense of the separate approximating neural network use.