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| Abstract |
A learning algorithm for single layer perceptrons is proposed. First, cone-like domains, each of which i s mapped by the perceptron network into almost an associative pattern, are derived. The learnin...g algorithm is obtained as a process that enlarges the cone-like domains. For autoassociative networks, it is shown that the cone-like domains become domains of attraction for stored patterns in the network. In this case, extended domains of attraction are also obtained by feeding t h e outputs of the network back to the input layer. I n computer simulations, character recognition ability of the autoassociative network i s examined.show more
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doi-tr-126