A comparative study for modeling of hot-rolled steel plate classification using a statistical approach and neural-net systems

Classification of flat steel products from the point of view of reaching the target property is a common practice in industries. In most classification problems, standard statistical methods generally place constraints such as continuous, differentiable, otherwise well behaved, etc. However, Artificial Neural Networks (ANN) has an ability to learn and generalize any complex system without making any model assumptions. This work emphasizes on making performance evaluation of usual statistical techniques such as general clustering like K-means, partition around medoid (PAM), classification and regression tree (CART), linear discriminant analysis (LDA) vis-a-vis usage of multilayer perceptron (MLP) learning algorithm, radial basis function (RBF) family of methods and Kohonen networks. To recommend the utility of modeling, some real-life industrial databases are used. It can be observed from the results that learning of networks through back-propagation yielded minimum misclassification of two groups of heats including minimization of train-test error. The statistical techniques such as LDA and CART provide the same results of misclassification along with the results obtained from perceptron learning, RBF network algorithm and Kohonen learning with learning-vector quantization (LVQ) algorithm.