Training with noise: application to word and text storage

We describe local iterative training algorithms, which maximise the number of stored patterns and their content-addressability in the Hopfield net and generalisations of it. Provided a solution exists to the problem of retrieving prescribed patterns from any initial configuration with a given number of wrong bits, the algorithms are shown to converge to one such solution. We describe an application to the storage of words and continuous text, exploiting the Distributed Array Processor.

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