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 Potts Neuron Approach to Communication Routing

Hakkinen1, Lagerholm, Peterson

  • 1University of Lund, Department of Theoretical Physics, Lund, SE, Solvegatan 14A, SE-223 62.

Neural Computation
|August 11, 1998
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

Prof. Moore's Clinic at Buffalo General Hospital.

Buffalo medical and surgical journal·2023
Same author

Study of B Decays to Charmonium States: B-->eta(c)K and B --> chi(c0)K.

Physical review letters·2001
Same author

N-Nitrosobenzylmethylamine is activated to a DNA benzylating agent in rats volume 10, number 1, january 1997, pp 19-26

Chemical research in toxicology·2000
Same author

TNF-alpha in trauma patients: a touch is just enough.

Current surgery·2000
Same author

Intra-aortic balloon counterpulsation pump therapy: a critical appraisal of the evidence for patients with acute myocardial infarction.

Critical care (London, England)·2000
Same author

Atheroablation Prior to Stents.

Current interventional cardiology reports·2000
Same journal

A Model-Free Reinforcement Learning Implementation of Decision Making Under Uncertainty by Sequential Sampling.

Neural computation·2026
Same journal

DROP: Distributional and Regular Optimism and Pessimism for Reinforcement Learning.

Neural computation·2026
Same journal

Hierarchical Active Inference Using Successor Representations.

Neural computation·2026
Same journal

W-Kernel and Its Principal Space for Frequentist Evaluation of Bayesian Estimators.

Neural computation·2026
Same journal

A Hidden Markov Model-Inspired Sequence Classification Method for Hyperdimensional Computing.

Neural computation·2026
Same journal

Sparse Graphical Modeling for Electrophysiological Phase-Based Connectivity Using Circular Statistics.

Neural computation·2026
See all related articles

This study introduces a novel neural network for communication routing, efficiently finding multiple shortest paths. The Potts neural network approach offers high-quality solutions for complex routing challenges.

Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Network Engineering

Background:

  • Communication routing is complex, especially with multiple simultaneous transmission requests.
  • Finding multiple shortest paths while preventing network overload is a significant challenge.

Purpose of the Study:

  • To develop a feedback neural network approach for communication routing problems.
  • To address multiple shortest path problems with multiple transmission requests.
  • To minimize path lengths and prevent network arc overloading.

Main Methods:

  • Utilized a set of Potts neurons for each transmission request.
  • Employed interactions to minimize path lengths and prevent arc overloading.
  • Used a propagator matrix approach to handle topological aspects.

Related Experiment Videos

  • Developed an algorithm based on local information for distributed implementation.
  • Compared results to exact (branch-and-bound) and heuristic solutions.

    • Main Results:

    • The Potts neural network approach yielded high-quality, feasible solutions for most tested problems.
    • The method demonstrated efficiency, with computational demand scaling with requests, nodes, and arcs.
    • In single-request cases, the approach simplified to a fuzzy Bellman-Ford algorithm.

    Conclusions:

    • The feedback neural network approach is effective for communication routing and multiple shortest path problems.
    • The method's reliance on local information facilitates distributed implementations.
    • This approach offers a promising solution for efficient and robust network routing.