Software sensor design using Bayesian automatic classification and back-propagation neural networks

Back-propagation neural networks (BPNN) have attracted attention as an effective method for designing software sensors. A critical issue with BPNN is the danger of extrapolation beyond the parameter space used for the training data. It is therefore important to select the data for model development and test with some care. This also means that there is a need to know when the BPNN model needs to be retrained with new data during use. This paper describes an approach for addressing this issue, which combines an unsupervised clustering method in conjunction with a BPNN model. An unsupervised Bayesian clustering system is used to automatically group the multivariate data into clusters in such a way that data patterns within a class have similar characteristics which distinguish them from other classes. Test data patterns for the BPNN model are selected from each class and when new data are available, the clustering system is employed to check if they are beyond the parameter space of the previous training data and therefore require retraining of the model. The approach is discussed by reference to the development of a software sensor of the fractionator of a refinery fluid catalytic cracking process.