ArticleEntropy (Basel, Switzerland)2021
Reservoir Computing with Delayed Input for Fast and Easy Optimisation.
Article in Entropy (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
- Triple-Mode Ferroelectric Thin-Film Transistor for Hybrid Electrical-Optical Reservoir Computing.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Modeling nonlinear oscillator networks using physics-informed hybrid reservoir computing.Scientific reports · 2025Article
- Post-processing methods for delay embedding and feature scaling of reservoir computers.Communications engineering · 2025Article
- Efficient optimisation of physical reservoir computers using only a delayed input.Communications engineering · 2025Article
- Ultrafast silicon photonic reservoir computing engine delivering over 200 TOPS.Nature communications · 2024Article
- Memory-Non-Linearity Trade-Off in Distance-Based Delay Networks.Biomimetics (Basel, Switzerland) · 2024Article
- Exploiting Signal Propagation Delays to Match Task Memory Requirements in Reservoir Computing.Biomimetics (Basel, Switzerland) · 2024Article
- Deriving task specific performance from the information processing capacity of a reservoir computer.Nanophotonics (Berlin, Germany) · 2023Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Reservoir computing is a machine learning method that solves tasks using the response of a dynamical system to a certain input. As the training scheme only involves optimising the weights of the responses of the dynamical system, this method is particularly suited for hardware implementation. Furthermore, the inherent memory of dynamical systems which are suitable for use as reservoirs mean that this method has the potential to perform well on time series prediction tasks, as well as other tasks with time dependence. However, reservoir computing still requires extensive task-dependent parameter optimisation in order to achieve good performance. We demonstrate that by including a time-delayed version of the input for various time series prediction tasks, good performance can be achieved with an unoptimised reservoir. Furthermore, we show that by including the appropriate time-delayed input, one unaltered reservoir can perform well on six different time series prediction tasks at a very low computational expense. Our approach is of particular relevance to hardware implemented reservoirs, as one does not necessarily have access to pertinent optimisation parameters in physical systems but the inclusion of an additional input is generally possible.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.