Description: Enter the study sample, using the back-propagation algorithm repeatedly adjust training of the network weights and deviation as close as possible, so that the output vector with expectation vector training is completed when the squared error of the network output layer is less than the specified error save the network weights and bias.
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BP-shenjinhMATLAB.doc
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