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

Incremental communication for adaptive resonance theory networks.

Ming Chen1, Ali A Ghorbani, Virendrakumar C Bhavsar

  • 1Faculty of Computer Science, University of New Brunswick, Fredericton, NB, E3B 5A3, Canada.

IEEE Transactions on Neural Networks
|March 1, 2005
PubMed
Summary

Limited precision incremental communication in artificial neural networks (ANNs) significantly reduces costs. This method, applied to adaptive resonance theory 2 (ART2) networks, maintains performance with 7-13-bit precision, enabling efficient VLSI implementation.

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

DeepSense: An Adaptive Scalable Ensemble Framework for Industrial IoT Anomaly Detection.

Sensors (Basel, Switzerland)·2026
Same author

CICIoT2023: A Real-Time Dataset and Benchmark for Large-Scale Attacks in IoT Environment.

Sensors (Basel, Switzerland)·2023
Same author

CapsRule: Explainable Deep Learning for Classifying Network Attacks.

IEEE transactions on neural networks and learning systems·2023
Same author

Understanding lignin-degrading reactions of ligninolytic enzymes: binding affinity and interactional profile.

PloS one·2011
Same author

[The expression and significance of Notch1 in the human kidney tubular cell transdifferentiation induced by TGF-β(1);].

Xi bao yu fen zi mian yi xue za zhi = Chinese journal of cellular and molecular immunology·2011
Same author

Construction of microRNA- and microRNA*-mediated regulatory networks in plants.

RNA biology·2011

Area of Science:

  • Artificial Intelligence
  • Computational Neuroscience
  • Computer Engineering

Background:

  • Artificial neural networks (ANNs) face challenges with high communication costs and learning times.
  • Incremental internode communication methods have been proposed to optimize ANN performance.
  • Adaptive Resonance Theory 2 (ART2) networks are a class of recurrent neural networks requiring efficient communication strategies.

Purpose of the Study:

  • To investigate the application of a limited precision incremental communication method to ART2 networks.
  • To evaluate the impact of this method on the convergence behavior and performance of ART2 networks.
  • To determine the optimal precision level for incremental communication in ART2 networks.

Main Methods:

  • Simulation studies were conducted to analyze the convergence of ART2 networks using the limited precision incremental communication method.

Related Experiment Videos

  • Theoretical error analysis was performed to understand the effects of limited precision on network performance.
  • Comparisons were made between results obtained with limited precision (7-13 bits) and full precision (32 bits).

    • Main Results:

    • Limited precision incremental communication (7-13 bits) yields results comparable to full precision (32 bits) in ART2 networks.
    • Simulation and analytical results demonstrate that limited precision errors are bounded and do not significantly impair network convergence.
    • The proposed method effectively reduces communication costs and learning time without compromising accuracy.

    Conclusions:

    • The limited precision incremental communication method is suitable for ART2 networks.
    • This approach enables efficient parallel and specialized Very Large Scale Integration (VLSI) implementations of ART2 networks.
    • The findings support the integration of incremental communication for optimizing the performance and hardware realization of ART2 networks.