| 摘要 |
A Neural Network using interconnecting weights each with two values, one of which is selected for use, can be taught to map a set of input vectors to a set of output vectors. A set of input vectors is applied to the network and in response, a set of output vectors is produced by the network. The error is the difference between desired outputs and actual outputs. The network is trained in the following manner. A set of input vectors is presented to the network, each vector being propogated forward through the network to produce an output vector. A set of error vectors is then presented to the network and propagated backwards. Each Tensor Weight Element includes a selective change means which accumulates particular information about the error. After all the input vectors are presented, an update phase is initiated. During the update phase, in accordance with the results of the derived algorithm, the selective change means selects the other weight value if selecting the other weight value will decrease the total error. Only one such change is made per set. After the update phase, if a selected value was changed, the entire process is repeated. When no values are switched, the network has adapted as well as it can, and the training is completed.
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