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

Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

140
A Y-connected synchronous generator, grounded through a neutral impedance, is designed to produce balanced internal phase voltages with only positive-sequence components. The generator's sequence networks include a source voltage that is exclusively in the positive-sequence network. The sequence components of line-to-ground voltages at the generator terminals illustrate this configuration.
Zero-sequence current induces a voltage drop across the generator's neutral impedance and other...
140
Block Diagram Reduction01:22

Block Diagram Reduction

285
The process of deriving the transfer function of a control system often involves reducing its block diagram to a single block. This simplification can be achieved through a series of strategic operations, including relocating branch points and comparators. These operations preserve the overall function of the system while allowing for easier manipulation and combination of blocks.
The first step in this process is the identification and relocation of a branch point. A branch point, where a...
285
Neural Circuits01:25

Neural Circuits

1.6K
Neural circuits and neuronal pools are two of the main structures found in the nervous system. Neural circuits are networks of neurons that work together to carry out a specific task or process. They consist of interconnected neurons and glial cells, which provide structural and metabolic support.
Neuronal pools are collections of nerve cells with similar functions and interact through chemical and electrical signals. These pools include both interneurons (the central neural circuit nodes that...
1.6K
Multimachine Stability01:25

Multimachine Stability

229
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:
229
Relation between Mathematical Equations and Block Diagrams01:20

Relation between Mathematical Equations and Block Diagrams

897
In a spring-mass-damper system, the second-order differential equation describes the dynamic behavior of the system. When transformed into the Laplace domain under zero initial conditions, this equation can be effectively analyzed and manipulated. The transformation into the Laplace domain converts differential equations into algebraic equations, simplifying the process of isolating the output.
897
Elements of Block Diagrams01:25

Elements of Block Diagrams

377
Block diagrams serve as a visual representation of the input-output relationships within a system. An illustrative example is a heating system, where the set temperature activates the furnace to warm the room to the desired level. Block diagrams are versatile, modeling linear systems through Laplace transform variables and nonlinear systems using time domain variables.
A block diagram typically includes essential elements such as comparators, blocks, and feedback loops. Each of these elements...
377

You might also read

Related Articles

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

Sort by
Same author

CQD-Modified SrTiO<sub>3</sub> for Enhanced Photocatalytic CO<sub>2</sub> Reduction to Methane.

Materials (Basel, Switzerland)·2026
Same author

CaCO<sub>3</sub>/BiO<sub>2-x</sub>/CdS Composite with Rapid Photocatalytic Reduction of Cr(VI) Under Visible Light.

Nanomaterials (Basel, Switzerland)·2026
Same author

Recurrent Stochastic Configuration Networks With Hybrid Regularization for Nonlinear Dynamics Modeling.

IEEE transactions on cybernetics·2026
Same author

Theoretical Advances on Stochastic Configuration Networks.

IEEE transactions on neural networks and learning systems·2025
Same author

Investigation of the quality of life and influencing factors among perimenopausal women.

Archives of gynecology and obstetrics·2025
Same author

MOF-Based Electrocatalysts for Water Electrolysis, Energy Storage, and Sensing: Progress and Insights.

Chemical record (New York, N.Y.)·2025
Same journal

Exploiting audio-visual modalities in videos: Object detection via multi-stage bilateral coupling network.

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

Reliability-aware modality completion with cross-modal distillation for federated learning with missing modalities.

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

IGFD-Net: Illumination-guided frequency decoupling for polarization image fusion.

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

Multiple-Strategies dung beetle optimizer and its applications in engineering optimization and bankruptcy prediction.

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

Aggregating global-scale pixel-wise forgery cues within a graph.

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

Finite-Time intermittent control for secure synchronization of Neutral-Type stochastic delayed neural networks under aperiodic DoS attacks.

Neural networks : the official journal of the International Neural Network Society·2026
See all related articles

Related Experiment Video

Updated: Sep 10, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K

Recurrent stochastic configuration networks with block increments.

Dianhui Wang1, Gang Dang2

  • 1School of Data Science, Qingdao University of Science and Technology, Qingdao, 266061, China; State Key Laboratory of Synthetical Automation for Process Industries, Northeastern University, Shenyang, 110819, China.

Neural Networks : the Official Journal of the International Neural Network Society
|August 21, 2025
PubMed
Summary
This summary is machine-generated.

Block recurrent stochastic configuration networks (BRSCNs) enhance nonlinear dynamic system modeling by adding multiple subreservoirs. This approach improves learning efficiency and generalization for complex dynamics.

Keywords:
Block incrementsEcho state propertyPersistent excitation,Recurrent stochastic configuration networkUniversal approximation property

More Related Videos

Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.2K

Related Experiment Videos

Last Updated: Sep 10, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.3K
Designing and Implementing Nervous System Simulations on LEGO Robots
10:34

Designing and Implementing Nervous System Simulations on LEGO Robots

Published on: May 25, 2013

15.2K
Generation of Local CA1 &#947; Oscillations by Tetanic Stimulation
08:02

Generation of Local CA1 γ Oscillations by Tetanic Stimulation

Published on: August 14, 2015

9.2K

Area of Science:

  • Computational neuroscience
  • Machine learning
  • Nonlinear dynamics

Background:

  • Recurrent stochastic configuration networks (RSCNs) are effective for nonlinear dynamic systems with order uncertainty.
  • Existing RSCNs offer ease of implementation, reduced human intervention, and strong approximation capabilities.

Purpose of the Study:

  • Introduce block recurrent stochastic configuration networks (BRSCNs) to improve learning capacity and efficiency.
  • Enhance the modeling of complex nonlinear dynamic systems.

Main Methods:

  • Develop BRSCNs capable of adding multiple reservoir nodes (subreservoirs) simultaneously.
  • Configure each subreservoir with unique structures using a supervisory mechanism.
  • Scale the reservoir feedback matrix to ensure the echo state property.
  • Employ online output weight updates via a projection algorithm.
  • Establish persistent excitation conditions for parameter convergence.

Main Results:

  • BRSCNs demonstrate superior modeling efficiency and learning performance.
  • The proposed method shows favorable generalization performance across various tasks.
  • Effectiveness validated on time series prediction, nonlinear system identification, and industrial data analysis.

Conclusions:

  • BRSCNs offer significant potential for modeling complex dynamics with enhanced efficiency.
  • The novel architecture improves upon traditional RSCNs for dynamic system analysis.
  • BRSCNs provide a robust framework for tackling challenging nonlinear modeling problems.