DYNAMICAL AND COMPLEXITY RESULTS FOR HIGH ORDER NEURAL NETWORKS
1994 ◽
Vol 05
(03)
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pp. 241-252
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We present dynamical results concerning neural networks with high order arguments. More precisely, we study the family of block-sequential iteration of neural networks with polynomial arguments. In this context, we prove that, under a symmetric hypothesis, the sequential iteration is the only one of this family to converge to fixed points. The other iteration modes present a highly complex dynamical behavior: non-bounded cycles and simulation of arbitrary non-symmetric linear neural network.5 We also study a high order memory iteration scheme which accepts an energy functional and bounded cycles in the size of the memory steps.
2008 ◽
Vol 18
(05)
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pp. 1343-1361
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1978 ◽
Vol 36
(1)
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pp. 330-331
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