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

Extension neural network-type 2 and its applications.

Mang-Hui Wang1

  • 1Institute of Information and Electrical Energy, National Chin-Yi Institute of Technology, Taichung, Taiwan, ROC. wangmh@chinyi.ncit.edu.tw

IEEE Transactions on Neural Networks
|December 14, 2005
PubMed
Summary

The new extension neural network type 2 (ENN-2) offers unsupervised pattern clustering without initial parameters. This novel approach uses extension distance (ED) for efficient and stable data clustering, outperforming traditional methods.

Related Concept Videos

You might also read

Related Articles

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

Sort by
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

Area of Science:

  • Artificial Intelligence
  • Machine Learning
  • Computational Neuroscience

Background:

  • Supervised learning pattern classification has been established with the extension neural network (ENN).
  • Unsupervised learning methods are crucial for complex data analysis and pattern discovery.
  • Existing clustering algorithms often require predefined parameters, limiting their applicability.

Purpose of the Study:

  • To introduce the extension neural network type 2 (ENN-2), an unsupervised learning algorithm for pattern clustering.
  • To present a novel clustering approach that does not require initial cluster number or center estimations.
  • To demonstrate the stability and plasticity of the ENN-2, mimicking human memory systems.

Main Methods:

  • The proposed extension neural network type 2 (ENN-2) utilizes an extension distance (ED) metric.

Related Experiment Videos

  • Clustering is controlled by a distance parameter and the novel extension distance.
  • The algorithm learns meaningful weights and adapts to data without pre-set initializations.

    • Main Results:

    • The ENN-2 demonstrates effective pattern clustering across diverse datasets.
    • The network exhibits stability and plasticity, akin to human memory.
    • Experimental results validate the effectiveness and applicability of ENN-2 in benchmark and practical scenarios.

    Conclusions:

    • The ENN-2 provides a robust and efficient unsupervised clustering solution.
    • Its simplified structure and shorter learning time offer advantages over traditional neural networks.
    • The method's ability to adapt without initial parameters enhances its versatility.