Learn More
Anomaly detectors (or novelty detectors) are systems for detecting behaviour that deviates from "normality ", and are useful in a wide range of surveillance, monitoring and diagnosis applications. Feed-forward auto-associative neural networks have, in several studies, shown to be effective anomaly detectors although they have a tendency to produce false(More)
Making use of the knowledge that these diverse strategies provide would require us to look at structural design, connectionist versus non-connectionist combination methodologies, and possibly any neurophysiological equivalence. The limitations of stand-alone neural network systems have initiated research into multiple neural network systems (multinets),(More)
Automatic classification of modulation schemes is of interest for both civilian and military applications. This report describes an experiment classifying six modulation schemes using a Multi-Layered Perceptron (MLP) neural network. Six key features were extracted from the signals and used as inputs to the MLP. The approach was similar to that of Azzouz and(More)
  • 1