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Related Experiment Videos

An on-line algorithm for creating self-organizing fuzzy neural networks.

Gang Leng1, Girijesh Prasad, Thomas Martin McGinnity

  • 1Intelligent Systems Engineering Laboratory, School of Computing and Intelligent Systems, University of Ulster at Magee, Derry, Northern Ireland BT48 7JL, UK. gang_leng@bigfoot.com

Neural Networks : the Official Journal of the International Neural Network Society
|November 16, 2004
PubMed
Summary

This study introduces a self-organizing fuzzy neural network (SOFNN) algorithm for creating accurate fuzzy models. The SOFNN automatically determines its structure and parameters using novel learning techniques.

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Area of Science:

  • Artificial Intelligence
  • Computational Intelligence
  • Machine Learning

Background:

  • Fuzzy models are essential for complex system modeling.
  • Developing accurate and compact fuzzy models can be challenging.
  • Existing methods may require manual structure and parameter tuning.

Purpose of the Study:

  • To present a novel on-line algorithm for creating self-organizing fuzzy neural networks (SOFNNs).
  • To implement singleton or Takagi-Sugeno (TS) type fuzzy models.
  • To develop automated structure and parameter learning for fuzzy neural networks.

Main Methods:

  • Utilized ellipsoidal basis function (EBF) neurons with center and width vectors.
  • Developed new structure learning and parameter learning methods.

Related Experiment Videos

  • Employed adding/pruning techniques and a recursive on-line learning algorithm.
  • Provided a convergence proof for estimation error and linear network parameters.

    • Main Results:

    • The proposed SOFNN algorithm demonstrated high accuracy and a compact network structure.
    • The algorithm effectively self-organizes, automatically determining network structure and parameters.
    • Simulation results confirmed the simplicity and effectiveness of the developed methods.

    Conclusions:

    • The developed on-line algorithm enables automatic creation of accurate fuzzy neural models.
    • SOFNNs offer a powerful approach for self-organizing fuzzy system design.
    • The proposed methods are simple, effective, and yield high-accuracy compact models.