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A new learning algorithm-forward propagation (FP) of multilayered feedforward neural networks is presented. The authors show that as an associative memory the network constructed by the FP algorithm has several advantages. (1) Each training sample is an attractive center. (2) The attractive radius of each training sample reaches the maximum. (3) There is no spurious attractive center in the network...
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