Adaptive classification of temporal signals in fixed-weights recurrent neural networks: an existence proof

Ivan Tyukin; Danil Prokhorov; Cees van Leeuwen

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arXiv · 0705.3370

Adaptive classification of temporal signals in fixed-weights recurrent neural networks: an existence proof

Ivan Tyukin·Danil Prokhorov·Cees van Leeuwen

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Abstract

We address the important theoretical question why a recurrent neural network with fixed weights can adaptively classify time-varied signals in the presence of additive noise and parametric perturbations. We provide a mathematical proof assuming that unknown parameters are allowed to enter the signal nonlinearly and the noise amplitude is sufficiently small.

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BibTeX RIS

Ivan Tyukin, Danil Prokhorov, Cees van Leeuwen. 2007-05-23. Adaptive classification of temporal signals in fixed-weights recurrent neural networks: an existence proof. https://arxiv.org/abs/0705.3370

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