Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

Control Systems01:10

Control Systems

1.2K
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...
1.2K
Conservation of Energy in Control Volume01:14

Conservation of Energy in Control Volume

842
Consider a turbine operating under steady-flow conditions. The control volume is drawn around the turbine, with fluid entering at one point and exiting at another. The turbine extracts energy from the fluid, which performs mechanical work (shaft work).
For steady flow systems, the time derivative of the stored energy becomes zero since there is no energy accumulation within the control volume. This simplifies the energy equation to:
842
Control Volume and System Representations01:16

Control Volume and System Representations

1.2K
Two key frameworks are employed to analyze mass, energy, and momentum transfer: the control volume approach and the system approach. These frameworks offer different perspectives, depending on whether the focus is on a specific region in space (control volume approach) or a defined mass of fluid (system approach).
The control volume approach considers a stationary region in space through which fluid flows. This region is bounded by a control surface.  For instance, in the case of water...
1.2K
Cyclic Processes And Isolated Systems01:19

Cyclic Processes And Isolated Systems

2.8K
A thermodynamic system with zero heat exchange and work is an isolated system. For these systems, the internal energy remains constant.
In the case of a non-isolated system, the change in the internal energy is zero only if the process is cyclic. A thermodynamic process is considered cyclic if the system undergoes a series of changes and returns to its initial state. 
Consider a cyclic process that returns to its initial state, undergoing a four-step process. The heat transfer along each...
2.8K
Feedback control systems01:26

Feedback control systems

315
Feedback control systems are categorized in various ways based on their design, analysis, and signal types.
Linear feedback systems are theoretical models that simplify analysis and design. These systems operate under the principle that their output is directly proportional to their input within certain ranges. For instance, an amplifier in a control system behaves linearly as long as the input signal remains within a specific range. However, most physical systems exhibit inherent nonlinearity...
315
Entropy Change in Reversible Processes01:10

Entropy Change in Reversible Processes

2.6K
In the Carnot engine, which achieves the maximum efficiency between two reservoirs of fixed temperatures, the total change in entropy is zero. The observation can be generalized by considering any reversible cyclic process consisting of many Carnot cycles. Thus, it can be stated that the total entropy change of any ideal reversible cycle is zero.
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
2.6K

You might also read

Related Articles

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

Sort by
Same author

Ordinal patterns for characterization of transition to extreme events.

Chaos (Woodbury, N.Y.)·2026
Same author

Stochastic bifurcation and safety basin study of nonlinear vibration systems in Li-doped graphene nanoplates with time delays.

Chaos (Woodbury, N.Y.)·2025
Same author

Synchronization of spring pendula.

Chaos (Woodbury, N.Y.)·2025
Same author

Hopf-like bifurcations and multistability in a class of 3D Filippov systems with generalized Liénard's form.

Chaos (Woodbury, N.Y.)·2024
Same author

Complexity measure of extreme events.

Chaos (Woodbury, N.Y.)·2024
Same author

Unfolding the distribution of periodicity regions and diversity of chaotic attractors in the Chialvo neuron map.

Chaos (Woodbury, N.Y.)·2024

Related Experiment Video

Updated: Jul 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

583

Control of chaotic systems through reservoir computing.

Zi-Fei Lin1,2, Yan-Ming Liang1,3, Jia-Li Zhao1,4

  • 1School of Mathematics, Xi'an University of Finance and Economics, Xi'an 710100, People's Republic of China.

Chaos (Woodbury, N.Y.)
|December 11, 2023
PubMed
Summary

This study demonstrates that reservoir computing effectively controls chaos in nonlinear dynamic systems, even with random noise. Optimizing neural network parameters enhances machine learning performance for chaos control.

More Related Videos

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

404
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.4K

Related Experiment Videos

Last Updated: Jul 8, 2025

Large Scale Energy Efficient Sensor Network Routing Using a Quantum Processor Unit
05:30

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

Published on: September 8, 2023

583
Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator
06:04

Experimental Investigation of the Hierarchical Control in DC Microgrids Using a Real-time Simulator

Published on: February 14, 2025

404
Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
11:54

Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface

Published on: May 8, 2021

4.4K

Area of Science:

  • Nonlinear Dynamics
  • Computational Neuroscience
  • Machine Learning

Background:

  • Chaos is a ubiquitous dynamic feature in deterministic and stochastic nonlinear systems.
  • Controlling chaos is a significant challenge across various scientific fields.
  • Recurrent neural networks offer fast and accurate solutions for nonlinear dynamics problems.

Purpose of the Study:

  • To employ reservoir computing for controlling chaos in dynamic systems.
  • To assess the applicability of the method to systems with random noise.
  • To investigate the impact of neural network parameters on performance.

Main Methods:

  • Utilizing reservoir computing, a machine learning technique, to implement chaos control.
  • Incorporating a control term into the reservoir calculation algorithm.
  • Analyzing the effects of varying neuron counts and leakage rates.

Main Results:

  • The reservoir computing algorithm successfully controlled chaotic phenomena in dynamic systems.
  • The proposed method demonstrated robustness in the presence of random noise.
  • Appropriate construction of neural networks, by tuning parameters, improved performance.

Conclusions:

  • Reservoir computing provides an effective approach for chaos control in nonlinear systems.
  • The method is adaptable to noisy environments, broadening its applicability.
  • Optimizing neural network architecture is crucial for enhancing machine learning-based chaos control.