Adding Filters to Improve Reservoir Computer Performance

TL;DR

This paper explores enhancing reservoir computer performance by adding linear filters to its output, demonstrating potential improvements through simulations in signal fitting, prediction, and classification tasks.

Contribution

It introduces a novel method of expanding reservoir computers with output filters, which are easy to implement and can improve performance without complex network modifications.

Findings

Filters improve reservoir computer accuracy in simulations

Filtering enhances performance in signal fitting tasks

Filtering benefits observed in prediction and classification tasks

Abstract

Reservoir computers are a type of neuromorphic computer that may be built a an analog system, potentially creating powerful computers that are small, light and consume little power. Typically a reservoir computer is build by connecting together a set of nonlinear nodes into a network; connecting the nonlinear nodes may be difficult or expensive, however. This work shows how a reservoir computer may be expanded by adding functions to its output. The particular functions described here are linear filters, but other functions are possible. The design and construction of linear filters is well known, and such filters may be easily implemented in hardware such as field programmable gate arrays (FPGA's). The effect of adding filters on the reservoir computer performance is simulated for a signal fitting problem, a prediction problem and a signal classification problem.