V. Turchenko (Ukraine), C. Triki (Italy), and A. Sachenko (Ukraine)
Neural network, parallel training, historical data, high performance computing.
There is considered the application of neural networks
for accuracy improvement of sensor signal processing by
sensor drift prediction. The two methods of sensor drift
prediction are described. The main characteristics and
training time of integrating historical data neural network
are presented on the uniprocessor computer Pentium-III
600-128 Mb RAM. The two paralleling schemes of
training of integrating historical data neural network are
proposed. The fulfilled experimental researches of parallel
programs on the high-performance computer Origin2000
are shown high efficiency of second paralleling scheme.