The autoassociative neural network in signal analysis: III. Enhancing the reliability of a NN with application to a BWR

This is the third and last of a series of papers trying to unveil the opaqueness of neural networks structure through a geometrical approach [Marseguerra M., Zoia, A., 2005a. The autoassociative neural network in signal analysis: I. The data dimensionality reduction and its geometric interpretation. Ann. Nucl. Energy 32, 1191-1206, Marseguerra, M., Zoia, A., 2005b. The autoassociative neural network in signal analysis: II. Application to on-line monitoring of a simulated BWR component. Ann. Nucl. Energy 32, 1207-1223]. Artificial neural networks (NN) provide a powerful tool in the operation of complex systems, such as nuclear power plants, in that they are suitable to determine the relationship between measured variables and control parameters on the basis of input-output examples. However, their major drawback is the fact that they always provide an output to the user, regardless of the appropriateness of the input. In this paper, we propose to adopt an autoassociative neural network (AANN) to work in cooperation with the NN to first assess the well-posedness of the desired neural model and to successively establish the appropriateness of the input data. The neural algorithm has been applied to a nuclear problem: the estimation of the reactivity forcing function parameters from the values of the measured neutron flux in a BWR reactor (provided by a reduced-order literature model). In this example, the AANN was able to suggest through geometrical considerations how to decompose the dataset in order to obtain a successful training for the NN and thereafter to validate the input data, thus enhancing the reliability of the NN model output.