Garth S. Barbour

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A novel artificial neural network, derived from neurobiological observations, is described and examples of its performance are presented. This DYnamically STable Associative Learning (DYSTAL) network associatively learns both correlations and anticorrelations, and can be configured to classify or restore patterns with only a change in the number of output(More)
Dystal, an artificial neural network, was used to classify orange juice products. Nine varieties of oranges collected from six geographical regions were processed into single-strength, reconstituted or frozen concentrated orange juice. The data set represented 240 authentic and 173 adulterated samples of juices; 16 variables [8 flavone and flavanone(More)
One of the central problems in image recognition is the extraction of salient ‘‘features’’ in a manner robust to variation in position, orientation, and scale and suitable for further processing. Because real-world images contain distinct features at various resolutions, effective extraction may require the combination of edge and other information across(More)
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