Artificial neural systems are very powerful tools for pattern matching, classification, feature extraction and signal analysis. Systems to date lack an essential feature of their biological counterparts, a measure of confidence that the network response has actually been trained and is not an artifact. In the proposed artificial neural system one output is a produced (trained) measure of confidence in the remaining outputs i.e. a measure of certainty that the inputs match the training data.
COGNITIVE PATTERN MATCHING SYSTEM WITH BUILT-IN CONFIDENCE MEASURE