SARLA: a supervised adaptive resonance learning algorithm: principles and applications

In this paper we present a two layer neural network architecture based on the Adaptive Resonance Theory (ART) paradigm. It merges the unsupervised training capability of an ART-like structure with the supervised MLP algorithm by adapting the selective attention mechanisms to the pattern-class characteristics. It has been successfully applied to pattern recognition tasks such as isolate word recognition.