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Abstract

This paper examines the efficiency of a generalized adaptive neural network algorithm (GANNA) processor in comparison to earlier model-based methods, a back-propagation artificial neural network, and logistic regression approaches to data classification. The research uses the binary classification problem of discriminating between failing and non-failing firms to compare the methods. The results indicate the potential in time savings and the successful classification results available from a GANNA processor.