Abstract
The main feature of neural network using for accuracy improvement of physical quantities (for example, temperature, humidity, pressure etc.) measurement by data acquisition systems is insufficient volume of input data for predicting neural network training at an initial exploitation period of sensors. The authors have proposed the technique of data volume increasing for predicting neural network training using integration of historical data method. In this paper we have proposed enhanced integration historical data method with its simulation results on mathematical models of sensor drift using single-layer and multi-layer perceptrons. We also considered a parallelization technique of enhanced integration historical data method in order to decrease its working time. A modified coarse-grain parallel algorithm with dynamic mapping on processors of parallel computing system using neural network training time as mapping criterion is considered. Fulfilled experiments have showed that modified parallel algorithm is more efficient than basic parallel algorithm with dynamic mapping, which does not use any mapping criterion.
Highlights
The authors have shown in [1,2], that the error of modern sensor data acquisition systems is much less than sensor’s error in many cases
Parallel algorithm with dynamic mapping [12] is developed by using a “centralized” planning approach with only one processor Master having the role of task planner and each of the other processors will train the Integrating Historical Data Neural Networks (IHDNNs) assigned by the Master
The experimental results have been collected by using the computational grid with Globus middleware [21,22]
Summary
The authors have shown in [1,2], that the error of modern sensor data acquisition systems is much less than sensor’s error in many cases. Additional approximating neural network and integration of historical data using set of Integrating Historical Data Neural Networks (IHDNNs), have been proposed and experimentally investigated in [2, 7, 9,10] These methods allow considerably decreasing number of sensor calibration/testing by artificial increasing of the training set of predicting neural network. Experimental results of these methods showed [2, 4] that they allow increasing an accuracy of sensor drift prediction in 3-5 times at simultaneous increasing of inter-testing interval in 6-12 times.
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