# Reservoir Computing with Noise

## TL;DR

This paper explores how noise impacts reservoir computing performance, revealing that matching noise levels during training and testing and applying low-pass filtering can mitigate adverse effects.

## Contribution

It provides a detailed analysis of noise effects on reservoir computing and proposes practical noise mitigation strategies like filtering.

## Key findings

- Optimal performance occurs when training and testing noise levels are equal.
- Low-pass filtering input and signals preserves performance under noise.
- Filtering reduces noise effects without degrading reservoir accuracy.

## Abstract

This paper investigates in detail the effects of noise on the performance of reservoir computing. We focus on an application in which reservoir computers are used to learn the relationship between different state variables of a chaotic system. We recognize that noise can affect differently the training and testing phases. We find that the best performance of the reservoir is achieved when the strength of the noise that affects the input signal in the training phase equals the strength of the noise that affects the input signal in the testing phase. For all the cases we examined, we found that a good remedy to noise is to low-pass filter the input and the training/testing signals; this typically preserves the performance of the reservoir, while reducing the undesired effects of noise.