A hybrid model of partial least squares and artificial neural network for analyzing process monitoring data

Due to the advancement of data acquisition technology, a vast amount of process monitoring data can be easily gathered at most manufacturing sites. However, analyzing such data is difficult in that they usually consist of many variables correlated with each other. The partial least squares (PLS) method or artificial neural network (ANN) is known to be useful for analyzing such process monitoring data. In the article, a hybrid model of PLS and ANN is developed for increasing prediction performance, reducing the training time, and simplifying the ANN structure for analyzing process monitoring data. Computational results indicate that the proposed hybrid approach is a promising alternative to the usual PLS or ANN for analyzing process monitoring data. The proposed approach also results in a simpler optimum structure and can be generally trained faster than the ordinary ANN.