Fuzzy neural networks for classification and detection of anomalies

A new learning algorithm for the Simpson fuzzy min-max neural network is presented. It overcomes some undesired properties of the Simpson model. Our new algorithm improves the network performance; the classification result does not depend on the presentation order of the patterns in the training set, and at each step, the classification error in the training set cannot increase. The new neural model is particularly useful in classification problems. Tests were executed on three different classification problems: 1) with two-dimensional synthetic data; 2) with realistic data generated by a simulator to find anomalies in the cooling system of a blast furnace; and 3) with real data for industrial diagnosis. The experiments were made following some recent evaluation criteria known in the literature and by using Microsoft Visual C++ development environment on personal computers.