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 Experiment Videos

A delayed neural network for solving linear projection equations and its analysis.

Qingshan Liu1, Jinde Cao, Youshen Xia

  • 1Department of Mathematics, Southeast University, Nanjing 210096, China.

IEEE Transactions on Neural Networks
|August 27, 2005
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

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

Sort by
Same author

The coherence analysis and Laplacian spectrum applications of cycle-based iterative networks.

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

Optimized predefined-time control for high-order nonlinear MASs via ICA and reinforcement learning.

ISA transactions·2026
Same author

Data-Driven Optimized Output Regulation for Markov Jump Linear Systems and Its Application.

IEEE transactions on cybernetics·2026
Same author

Turing instability and pattern formation in a diffusive predator-prey system with opportunistic predators and weak Allee effect.

Physical review. E·2026
Same author

Stochastic-Sampling-Based Event-Triggered Control for Switching Reaction-Diffusion Neural Networks.

IEEE transactions on cybernetics·2026
Same author

Passivity and synchronization of fractional-order coupled neural networks with multiple weights: A PD approach.

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

A novel delayed neural network effectively solves linear projection equations and quadratic programming problems. This approach enhances stability analysis using Lyapunov-Krasovskii theory and linear matrix inequality methods.

Area of Science:

  • Computational Mathematics
  • Neural Networks
  • Control Theory

Background:

  • Linear projection equations and quadratic programming problems are fundamental in various scientific and engineering domains.
  • Existing methods for solving these problems may have limitations in terms of efficiency or stability.
  • Functional differential equations introduce complexities in system analysis and control.

Purpose of the Study:

  • To introduce a delayed neural network (DNN) model for solving linear projection equations.
  • To analyze the global asymptotic and exponential stability of the proposed DNN.
  • To demonstrate the effectiveness of the DNN for solving quadratic programming problems.

Main Methods:

  • Utilizing Lyapunov-Krasovskii theory for stability analysis of functional differential equations.

Related Experiment Videos

  • Employing the linear matrix inequality (LMI) approach for stability verification.
  • Developing and simulating a delayed neural network architecture.

    • Main Results:

    • The proposed delayed neural network demonstrates global asymptotic and exponential stability.
    • Theoretical analysis confirms the stability properties of the DNN.
    • Illustrative examples show the DNN's superior performance in solving linear projection equations and quadratic programming problems compared to existing methods.

    Conclusions:

    • The delayed neural network offers an effective and stable approach for solving linear projection equations.
    • The DNN is also capable of solving certain quadratic programming problems.
    • This work contributes a new tool for computational mathematics and control theory.