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

State Space Representation01:27

State Space Representation

The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
Linear time-invariant Systems01:23

Linear time-invariant Systems

A system is linear if it displays the characteristics of homogeneity and additivity, together termed the superposition property. This principle is fundamental in all linear systems. Linear time-invariant (LTI) systems include systems with linear elements and constant parameters.
The input-output behavior of an LTI system can be fully defined by its response to an impulsive excitation at its input. Once this impulse response is known, the system's reaction to any other input can be calculated...
BIBO stability of continuous and discrete -time systems01:24

BIBO stability of continuous and discrete -time systems

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.
Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

Nonlinear systems often require sophisticated approaches for accurate modeling and analysis, with state-space representation being particularly effective. This method is especially useful for systems where variables and parameters vary with time or operating conditions, such as in a simple pendulum or a translational mechanical system with nonlinear springs.
For a simple pendulum with a mass evenly distributed along its length and the center of mass located at half the pendulum's length, the...
Classification of Systems-I01:26

Classification of Systems-I

Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
Sequence Networks of Rotating Machines01:24

Sequence Networks of Rotating Machines

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...

You might also read

Related Articles

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

Sort by
Same author

Plant-derived mitochondria mitigate aging-related neurodegeneration by reprogramming microglial mitochondrial energy metabolism.

Translational neurodegeneration·2026
Same author

Metabolic syndrome-associated gut microbiota and plasma metabolite profiles in schizophrenia.

Translational psychiatry·2026
Same author

DLP bioprinting of cartilage organoid-laden bioinks yields high-fidelity auricular constructs with enhanced chondrogenesis.

Stem cell research & therapy·2026
Same author

An Oxazine-Locked Covalent Organic Framework by a Tandem Pinner/Schiff Base Reaction for Hydrogen Peroxide Photosynthesis.

Journal of the American Chemical Society·2026
Same author

Key molecular mechanisms of mitochondrial metabolic pathways in specific cell subpopulations of pancreatic cancer based on scRNA-seq and bulk RNA-seq.

PloS one·2026
Same author

Application of Cerium-Tannic Acid-Formaldehyde Coordination Polymer Colloidal Nanomaterials to Alleviate Lipopolysaccharide-Induced Acute Lung Injury.

International journal of nanomedicine·2026

Related Experiment Videos

Dynamics of random Boolean networks under fully asynchronous stochastic update based on linear representation.

Chao Luo1, Xingyuan Wang

  • 1Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian, China.

Plos One
|June 21, 2013
PubMed
Summary
This summary is machine-generated.

This study introduces a new algebraic method to analyze asynchronous random Boolean networks (ARBNs). The research provides tools to find all system attractors and basins, advancing the understanding of complex network dynamics.

Related Experiment Videos

Area of Science:

  • Computational Biology
  • Network Science
  • Algebraic Dynamics

Background:

  • Asynchronous random Boolean networks (ARBNs) are complex systems with applications in modeling biological networks.
  • Understanding the dynamics and long-term behavior of ARBNs is crucial but challenging.
  • Existing methods often struggle with the asynchronous and random nature of these networks.

Purpose of the Study:

  • To develop a novel algebraic approach for analyzing the dynamics of ARBNs.
  • To provide a general formula for network transition matrices in ARBNs.
  • To establish criteria for identifying attractors and basins within ARBNs.

Main Methods:

  • Conversion of ARBN logical equations into a discrete-time linear representation.
  • Derivation of a general formula for network transition matrices.
  • Development of an algebraic criterion to identify attractors of a specific length.
  • Algorithm design for finding all attractors and basins.

Main Results:

  • A general formula for the network transition matrices of ARBNs was derived.
  • A necessary and sufficient algebraic criterion for identifying attractors was established.
  • Algorithms were successfully developed to find all attractors and basins in ARBNs.
  • Demonstrated feasibility through illustrative examples.

Conclusions:

  • The proposed algebraic approach offers an effective framework for studying ARBN dynamics.
  • The developed criteria and algorithms provide powerful tools for analyzing network attractors and basins.
  • This work contributes to a deeper understanding of complex system behaviors in ARBNs.