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- W2103702921 abstract "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. We also study a high order memory iteration scheme which accepts an energy functional and bounded cycles in the size of the memory steps." @default.
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- W2103702921 date "1994-09-01" @default.
- W2103702921 modified "2023-09-23" @default.
- W2103702921 title "DYNAMICAL AND COMPLEXITY RESULTS FOR HIGH ORDER NEURAL NETWORKS" @default.
- W2103702921 doi "https://doi.org/10.1142/s0129065794000256" @default.
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