Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Control Systems

Control Systems

Control systems are everywhere in contemporary society, influencing diverse applications from aerospace to automated manufacturing. These systems can be found naturally within biological processes, such as blood sugar regulation and heart rate adjustment in response to stress, as well as in man-made systems like elevators and automated vehicles. A control system is essentially a network of subsystems and processes that collaboratively convert specific inputs into desired outputs.
At the heart...

First Order Systems

First Order Systems

First-order systems, such as RC circuits, are foundational in understanding dynamic systems due to their straightforward input-output relationship. Analyzing their responses to different input functions under zero initial conditions reveals significant insights into system behavior.
When a first-order system is subjected to a unit-step input, its response is characterized by its transfer function. By applying the Laplace transform of the unit-step input to the transfer function, expanding the...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Nonlinear dynamics of reservoir computing: Theory, realization, and application.

Chaos (Woodbury, N.Y.)·2026

Same author

Prediction performance of random reservoirs with different topology for nonlinear dynamical systems with different number of degrees of freedom.

Chaos (Woodbury, N.Y.)·2026

Same author

Data-driven performance measures using global properties of attractors for testing black-box surrogate models of chaotic systems.

Chaos (Woodbury, N.Y.)·2025

Same author

Computational memory capacity predicts aging and cognitive decline.

Nature communications·2025

Same author

Post-processing methods for delay embedding and feature scaling of reservoir computers.

Communications engineering·2025

Same author

Deriving task specific performance from the information processing capacity of a reservoir computer.

Nanophotonics (Berlin, Germany)·2024

Same journal

Enhanced magnetic moment discrimination for multiplex nanoparticle quantification via dual-frequency nonlinearity probing.

Communications engineering·2026

Same journal

Ultrahigh-speed micromachining of sapphire by enhancing laser absorption.

Communications engineering·2026

Same journal

Industry-Academia Interface: Exploring the growth of Additive Manufacturing as an industry with Laura Del Río Fernández.

Communications engineering·2026

Same journal

Operating smart grids by customizing large model agents.

Communications engineering·2026

Same journal

Photovoltaics for space applications.

Communications engineering·2026

Same journal

EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers.

Communications engineering·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 1, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Efficient optimisation of physical reservoir computers using only a delayed input.

Enrico Picco¹, Lina Jaurigue², Kathy Lüdge²

¹Laboratoire d'Information Quantique CP224, Université libre de Bruxelles (ULB), Av. F. D. Roosevelt 50, 1050, Bruxelles, Belgium.

Communications Engineering

|January 18, 2025

Summary

This summary is machine-generated.

Reservoir computing, a machine learning method, is improved by using a delayed input signal. This technique enhances performance and simplifies hyperparameter tuning for time-dependent data processing.

More Related Videos

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Related Experiment Videos

Last Updated: Jun 1, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit

Published on: September 8, 2023

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Interactive and Visualized Online Experimentation System for Engineering Education and Research

Published on: November 24, 2021

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm

Published on: December 9, 2012

Area of Science:

Machine Learning
Optoelectronics
Nonlinear Dynamics

Background:

Reservoir computing is effective for time-dependent data but hyperparameter tuning is challenging.
Experimental implementation of reservoir computing requires efficient optimization methods.

Purpose of the Study:

To experimentally validate a novel optimization technique for reservoir computing.
To demonstrate the benefits of using a delayed input signal for reservoir computing systems.

Main Methods:

An optoelectronic setup with a fiber delay loop and a nonlinear node was used.
The reservoir received both the input signal and a delayed version of the input signal.
The system was tested on benchmark tasks under various operating conditions.

Main Results:

The delayed input method significantly improved reservoir computing performance.
This technique simplified the hyperparameter tuning process.
The experimental validation confirmed the effectiveness of the approach.

Conclusions:

The delayed input method is a viable and effective strategy for enhancing experimental reservoir computing.
This approach offers a practical solution to the challenges of hyperparameter optimization in reservoir computing.