Importance of input data normalization for the application of neural networks to complex industrial problems
- 1 June 1997
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Nuclear Science
- Vol. 44 (3), 1464-1468
- https://doi.org/10.1109/23.589532
Abstract
Recent advances in artificial intelligence have allowed the application of such technologies in real industrial problems. We have studied the application of backpropagation neural networks to several problems of estimation and identification in nuclear power plants. These problems often have been reported to be very time-consuming in the training phase. Among the different approaches suggested to ease the backpropagation training process, input data pretreatment has been pointed out, although no specific procedure has been proposed. We have found that input data normalization with certain criteria, prior to a training process, is crucial to obtain good results as well as to fasten significantly the calculations. This paper shows how data normalization affects the performance error of parameter estimators trained to predict the value of several variables of a PWR nuclear power plant. The criteria needed to accomplish such data normalization are also described.Keywords
This publication has 5 references indexed in Scilit:
- Intelligent control for a nuclear power plant using artificial neural networksPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Accelerating Learning of Neural Networks with Conjugate Gradients for Nuclear Power Plant ApplicationsNuclear Technology, 1994
- Incipient Fault Detection and Identification in Process Systems Using Accelerated Neural Network LearningNuclear Technology, 1994
- Application of neural networks to signal prediction in nuclear power plantIEEE Transactions on Nuclear Science, 1993
- Sensor validation for power plants using adaptive backpropagation neural networkIEEE Transactions on Nuclear Science, 1990