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

Linear Approximation in Time Domain01:21

Linear Approximation in Time Domain

75
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,...
75
Linear time-invariant Systems01:23

Linear time-invariant Systems

233
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...
233
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

45
Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
45
Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

369
Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
369
Difference Equation Solution using z-Transform01:24

Difference Equation Solution using z-Transform

271
The z-transform is a powerful tool for analyzing practical discrete-time systems, often represented by linear difference equations. Solving a higher-order difference equation requires knowledge of the input signal and the initial conditions up to one term less than the order of the equation.
The z-transform facilitates handling delayed signals by shifting the signal in the z-domain, which corresponds to delaying the signal in the time domain, and advancing signals by similarly shifting in the...
271
Ampere-Maxwell's Law: Problem-Solving01:17

Ampere-Maxwell's Law: Problem-Solving

574
A parallel-plate capacitor with capacitance C, whose plates have area A and separation distance d, is connected to a resistor R and a battery of voltage V. The current starts to flow at t = 0. What is the displacement current between the capacitor plates at time t? From the properties of the capacitor, what is the corresponding real current?
To solve the problem, we can use the equations from the analysis of an RC circuit and Maxwell's version of Ampère's law.
For the first part of...
574

You might also read

Related Articles

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

Sort by
Same author

VidCRAFT3: Camera, Object, and Lighting Control for Image-to-Video Generation.

IEEE transactions on visualization and computer graphics·2026
Same author

In situ TEM unveils the role of residual local strain on light-induced phase segregation in halide perovskites.

Science advances·2026
Same author

Effects of Methionine Supplementation in Low-Protein Diets on Growth Performance, Fur Quality, Blood Indices, and Intestinal Microbiota of Blue Foxes (<i>Vulpes lagopus</i>) During the Fur-Growing Period.

Animals : an open access journal from MDPI·2026
Same author

Preoperative gamma-glutamyl transferase to lymphocyte ratio predicts recurrence in non-muscle-invasive bladder cancer.

Frontiers in oncology·2026
Same author

A nomogram based on the red cell distribution width to lymphocyte ratio as a prognostic tool for non-muscle-invasive bladder cancer: a retrospective study.

Frontiers in oncology·2026
Same author

Preoperative fibrinogen-to-lymphocyte ratio as a prognostic biomarker for non-muscle-invasive bladder cancer.

Frontiers in oncology·2026

Related Experiment Video

Updated: Jun 16, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K

A novel discrete zeroing neural network for online solving time-varying nonlinear optimization problems.

Feifan Song1, Yanpeng Zhou2, Changxian Xu3

  • 1School of Finance, Changchun Finance College, Changchun, China.

Frontiers in Neurorobotics
|August 21, 2024
PubMed
Summary
This summary is machine-generated.

A new discrete zeroing neural network (DZNN) method optimizes shortest path planning by reformulating it as an energy minimization problem. This approach offers efficient and real-time solutions for complex navigation tasks.

Keywords:
0-stabilitydiscrete zeroing neural networkpath planningreal-time capabilitytime-varying nonlinear optimization problem

More Related Videos

Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
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

15.0K

Related Experiment Videos

Last Updated: Jun 16, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
06:45

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator

Published on: October 28, 2022

1.6K
Deep Neural Networks for Image-Based Dietary Assessment
13:19

Deep Neural Networks for Image-Based Dietary Assessment

Published on: March 13, 2021

9.0K
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

15.0K

Area of Science:

  • Artificial Intelligence
  • Robotics
  • Optimization Theory

Background:

  • Shortest path planning is crucial for efficient navigation in various applications.
  • Existing methods may face challenges with real-time computation and dynamic environments.

Purpose of the Study:

  • To introduce a novel discrete zeroing neural network (DZNN) method for shortest path planning.
  • To address the need for efficient and real-time solutions in dynamic environments.

Main Methods:

  • Reformulating the shortest path problem as an optimization problem.
  • Developing a discrete nonlinear function based on an energy function.
  • Utilizing a discrete zeroing neural network model (DZNNM) for solving the optimization problem.

Main Results:

  • The DZNN model demonstrates zero stability, effectiveness, and real-time performance.
  • Simulations confirm the efficiency and real-time capabilities of the DZNNM for time-varying nonlinear optimization problems (TVNOPs).

Conclusions:

  • The proposed DZNN method is suitable and superior for real-time shortest path planning.
  • The DZNNM effectively handles time-varying nonlinear optimization problems relevant to navigation.