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Hopfield network-- good at associative memory solution with the realization of lost H associative memory networks, are key to bringing the memory model samples corresponding network energy function of the minimum. With M-N-dimensional memory model, the network N neurons connect between right wij and N output threshold j design makes : M-mode memory corresponding to the network is a state network energy function is the M-000 minimum. More difficult, it is not an arbitrary form of adaptation memory model of effective, common design methods. H network algorithm 1) mode of learning-- decision weights want memory model, with 1 and 2 of the value of a model, said :-1, 1, 1, 1 ,1,1, ... in general : two were arbitrary neuron j i weights between : wij ap = (i) ap (j), p = 1 ... p; P : The tot
Packet : 962527hopfild1.rar filelist
hopfild1\hopfild1.vbp
hopfild1\hopfild1.vbw
hopfild1\login.frm
hopfild1\Module1.bas
hopfild1\prepare.frm
hopfild1\process.frm
hopfild1\process.log
hopfild1\run.frm
hopfild1\test.frm
hopfild1\工程1.vbw
hopfild1\新建 文本文档.txt
hopfild1\组1.vbg
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