Adaptive classification of temporal signals in fixed-weights recurrent neural networks: an existence proof
Ivan Tyukin, Danil V. Prokhorov, Cees van Leeuwen · arXiv (Cornell University) · 2007
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.