| 摘要 |
A perceptron device has processing elements arranged in an input layer, one or more hidden layers, and an output layer. The training method of the perceptron device uses the back propagation method. In order to increase learning speed, the learning rate for the updates of a particular processing element is a function f(M,N,K) of the number N of inputs to that element, the number K of outputs of that element, and the number N of inputs to the next layer. Function f increases with increasing M, and decreases with increasing K and N.
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