Simplified Fuzzy ARTMAP as Pattern Recognizer

Abstract

Pattern recognition has turned out to be an important aspect of a dominant technology such as machine intelligence. Domain specific fuzzy-neuro models particularly for the “black box” implementation of PR applications have been recently investigated. In this paper, Kasuba's simplified fuzzy adaptive resonance theory map (ARTMAP) has been discussed as a pattern recognizer/classifier for image processing problems. The model inherently recognizes only noise free patterns and in the case of patterns with noise or perturbations (rotation/scaling/translation) misclassifies the images. To tackle this problem, a conventional moment based rotation/scaling/translation invariant feature extractor has been employed. However, since the conventional feature extractor is not strictly invariant to most perturbations, certain mathematical modifications have been proposed that have resulted in an excellent performance by the pattern recognizer. The potential of the model has been demonstrated on two problems, namely, prediction of load from the yield patterns of elastoplastic analysis of clamped and simply supported plates and prediction of modes from mode shapes.