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

On multiple training for bidirectional associative memory.

Y F Wang1, J R Cruz, J R Mulligan

  • 1Dept. of Electr. and Comput. Eng., California Univ., Irvine, CA.

IEEE Transactions on Neural Networks
|January 1, 1990
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 First Report of Cucumber Mosaic Virus Infecting Water Chestnut in China.

Plant disease·2019
Same author

First Report of Alternaria alternata Causing Black Leaf Spot of Rubber Tree in China.

Plant disease·2019
Same author

[Adenocarcinoma arising from traditional serrated adenoma in the jejunum: report of a case].

Zhonghua bing li xue za zhi = Chinese journal of pathology·2019
Same author

Measurement of the Electron Antineutrino Oscillation with 1958 Days of Operation at Daya Bay.

Physical review letters·2019
Same author

Measurement of the Absolute Branching Fraction of the Inclusive Semileptonic Λ_{c}^{+} Decay.

Physical review letters·2019
Same author

Search for Subsolar-Mass Ultracompact Binaries in Advanced LIGO's First Observing Run.

Physical review letters·2018
Same journal

Universal perceptron and DNA-like learning algorithm for binary neural networks: LSBF and PBF implementations.

IEEE transactions on neural networks·2013
Same journal

Guest editorial: special section on white box nonlinear prediction models.

IEEE transactions on neural networks·2011
Same journal

Data-based fault-tolerant control of high-speed trains with traction/braking notch nonlinearities and actuator failures.

IEEE transactions on neural networks·2011
Same journal

Guest editorial: special section on data-based control, modeling, and optimization.

IEEE transactions on neural networks·2011
Same journal

Neural network-based multiple robot simultaneous localization and mapping.

IEEE transactions on neural networks·2011
Same journal

Data-driven model-free adaptive control for a class of MIMO nonlinear discrete-time systems.

IEEE transactions on neural networks·2011
See all related articles

This study determines the minimum training repetitions needed to ensure recall in bidirectional associative memory (BAM). Findings provide insights into efficient training strategies for associative learning systems.

Area of Science:

  • Cognitive science
  • Neuroscience
  • Machine learning

Background:

  • Bidirectional associative memory (BAM) models are crucial for understanding associative learning and memory recall.
  • Determining optimal training parameters is essential for the effective functioning of BAM systems.

Purpose of the Study:

  • To derive the minimal number of training repetitions required for guaranteed recall of specific pairs in a BAM.
  • To establish a theoretical framework for efficient BAM training.

Main Methods:

  • Mathematical derivation of training requirements.
  • Analysis of recall probability as a function of training instances.

Main Results:

  • A formula is presented for the minimal number of training uses per pair.

Related Experiment Videos

  • The derived number ensures a high probability of recalling trained pairs.

    • Conclusions:

    • The study provides a precise quantitative measure for BAM training efficiency.
    • This finding is applicable to optimizing learning algorithms and understanding memory consolidation.