Dynamical and complexity results for high order neural networks.
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.
Goles
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Universidad de Chile, Facultad de Ciencias Físicas y Matématicas, Departamento de Ingeniería Matéatica, Santiago.
Matamala
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D003198:Computer Simulation; D016571:Neural Networks, Computer