EXPLORATORY DATA-ANALYSIS - A COMPARISON OF STATISTICAL-METHODS WITH ARTIFICIAL NEURAL NETWORKS

Knowledge acquisition is perceived as a major bottleneck in the deployment of expert systems. This problem is more severe in knowledge-based control systems because the knowledge is often difficult to extract and needs to be constantly updated to reflect changing processing conditions. This article considers the problem of extracting processing knowledge directly from historical plant operational data. First we consider the features of the problem. The applicability of machine learning algorithms is considered. Quadratic regression, induction and artificial neural network modeling are applied to a process selected from composite manufacturing. The results show that, while neural networks are a promising new approach, there is a need to focus attention on the preprocessing of data to cope with data reduction and process nonlinearity. Methods based on statistical analysis will play an important role in this preprocessing.