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

TL;DR

This paper provides a mathematical proof demonstrating that fixed-weights recurrent neural networks can adaptively classify time-varying signals despite noise and parameter changes, under certain conditions.

Contribution

It offers an existence proof explaining the theoretical capability of fixed-weights RNNs to adaptively classify signals with unknown nonlinear parameters and small noise.

Findings

Fixed-weights RNNs can classify signals adaptively under certain conditions.

The proof assumes small noise amplitude and nonlinear parameter entry.

Theoretical validation of adaptive classification in fixed-weights RNNs.

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.