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

Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

454
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence of...
454
Multimachine Stability01:25

Multimachine Stability

601
Multimachine stability analysis is crucial for understanding the dynamics and stability of power systems with multiple synchronous machines. The objective is to solve the swing equations for a network of M machines connected to an N-bus power system.
In analyzing the system, the nodal equations represent the relationship between bus voltages, machine voltages, and machine currents. The nodal equation is given by:
601
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

1.0K
System stability is a fundamental concept in signal processing, often assessed using convolution. For a system to be considered bounded-input bounded-output (BIBO) stable, any bounded input signal must produce a bounded output signal. A bounded input signal is one where the modulus does not exceed a certain constant at any point in time.
To determine the BIBO stability, the convolution integral is utilized when a bounded continuous-time input is applied to a Linear Time-Invariant (LTI) system....
1.0K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

1.5K
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
1.5K
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

2.1K
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
2.1K
Transient and Steady-state Response01:24

Transient and Steady-state Response

618
In control systems, test signals are essential for evaluating performance under various conditions. The ramp function is effective for systems undergoing gradual changes, while the step function is suitable for assessing systems facing sudden disturbances. For systems subjected to shock inputs, the impulse function is the most appropriate test signal.
These test signals are integral in designing control systems to exhibit two key performance aspects: transient response and steady-state...
618

You might also read

Related Articles

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

Sort by
Same author

Study on a Novel Dental Composite Resin with Fluorinated Polyurethane Monomer and Modified Polyether Ether Ketone Fillers.

International dental journal·2026
Same author

Emulsifier hydrolysis, emulsion specific surface area and stability synergistically regulate lipid release behavior in OSA-EGCG systems.

Food chemistry·2026
Same author

Relaxed conditions and PSO-based optimization for the problem of Mittag-Leffler synchronization and its application in image restoration for fractional-order octonion-valued two-layer neural networks.

Neural networks : the official journal of the International Neural Network Society·2026
Same author

Tea polyphenol-soy lecithin-beeswax composite oleogelator for soybean oil oleogel: multiscale structural characterization and performance validation in bakery matrices.

Journal of the science of food and agriculture·2026
Same author

CRISPR decodes the RNA regulatory network in prostate cancer: A review from mechanisms to precision therapeutics.

Non-coding RNA research·2026
Same author

Fuzzy Neural Networks-Based Prescribed-Time Fault-Tolerant Cooperative Control of Second-Order Nonlinear Heterogeneous Multiagent Systems.

IEEE transactions on cybernetics·2026

Related Experiment Video

Updated: Mar 12, 2026

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
08:28

Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

Published on: March 3, 2023

1.7K

Coupled Impulsive Control for Multisynchronization of Multistable Stochastic Neural Networks Under Parameter

Xuemei Li, Song Zhu, Zhen Zhang

    IEEE Transactions on Neural Networks and Learning Systems
    |March 10, 2026
    PubMed
    Summary
    This summary is machine-generated.

    This study addresses multisynchronization for multistable stochastic neural networks (MSNNs) with time delays and uncertainties. A novel impulsive control strategy ensures synchronization, reducing control costs and verifying effectiveness in complex network dynamics.

    More Related Videos

    Author Spotlight: Combined Peripheral Nerve Stimulation and Controllable Pulse Parameter Transcranial Magnetic Stimulation to Probe Sensorimotor Control and Learning
    14:47

    Author Spotlight: Combined Peripheral Nerve Stimulation and Controllable Pulse Parameter Transcranial Magnetic Stimulation to Probe Sensorimotor Control and Learning

    Published on: April 21, 2023

    3.8K
    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

    5.2K

    Related Experiment Videos

    Last Updated: Mar 12, 2026

    Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms
    08:28

    Automated Multimodal Stimulation and Simultaneous Neuronal Recording from Multiple Small Organisms

    Published on: March 3, 2023

    1.7K
    Author Spotlight: Combined Peripheral Nerve Stimulation and Controllable Pulse Parameter Transcranial Magnetic Stimulation to Probe Sensorimotor Control and Learning
    14:47

    Author Spotlight: Combined Peripheral Nerve Stimulation and Controllable Pulse Parameter Transcranial Magnetic Stimulation to Probe Sensorimotor Control and Learning

    Published on: April 21, 2023

    3.8K
    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

    5.2K

    Area of Science:

    • Neuroscience
    • Control Theory
    • Applied Mathematics

    Background:

    • Multistable stochastic neural networks (MSNNs) exhibit complex dynamics relevant to realistic neural environments.
    • Time-varying delays and parameter uncertainties pose significant challenges in controlling MSNNs.
    • Achieving synchronization in complex networks is crucial for understanding emergent behaviors.

    Purpose of the Study:

    • To investigate the multisynchronization of multistable stochastic neural networks (MSNNs) under challenging conditions.
    • To develop a cost-effective control strategy for achieving synchronization in MSNN systems.
    • To derive sufficient conditions for multisynchronization applicable to both fixed and switching network topologies.

    Main Methods:

    • Construction of an MSNN model incorporating time-varying delays and parameter uncertainties.
    • Adoption of a coupled impulsive control strategy to manage network synchronization.
    • Development of a Lyapunov functional and application of the average impulsive interval concept.
    • Analysis under fixed and switching network topologies.

    Main Results:

    • Sufficient conditions for achieving multisynchronization of MSNNs were successfully derived.
    • The proposed impulsive control strategy effectively reduces control costs.
    • The control scheme's validity was confirmed via a numerical simulation.

    Conclusions:

    • The study provides a robust framework for analyzing and controlling multisynchronization in complex MSNNs.
    • The developed impulsive control method offers an efficient approach for synchronization in dynamic neural networks.
    • The findings contribute to the theoretical understanding and practical application of synchronization in stochastic neural systems.