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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)
of Japanese Kanji using principal component analysis as a preprocessor to an articial neural etwork.
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)
Analysis of a biolgically motivated neural network for character recognition. learning for multi-layer feed-forward neural networks using the conjugate gradient method. of japanese kanji using principal component analysis as a preprocessor to an articial neural network.