Abstract
This Paper Proposes a Method for the Identification of Evolving Fuzzy Takagi-Sugeno Systems based on the Optimally Pruned Extreme Learning Machine (Op-Elm) Methodology. We Describe Elm Which is a Simple Yet Accurate and Fast Learning Algorithm for Training Single-Hidden Layer Feed-Forward Artificial Neural Networks (SLFNS) with Random Hidden Neurons. We Then Describe the Op-Elm Methodology for Building Elm Models in a Robust and Generic Manner. Leveraging on the Previously Proposed Online Sequential Elm Method and the Op-Elm, We Propose an Identification Method for Self-Developing or Evolving Neuro-Fuzzy Systems. This Method Follows a Random Projection based Approach to Extracting Evolving Fuzzy Rule bases. a Comparison is Performed over a Diverse Collection of Datasets Against Well Known Evolving Neuro-Fuzzy Methods, Namely Denfis and Ets. It is Shown that the Method Proposed is Robust and Competitive in Terms of Accuracy and Speed. © 2010 IEEE.
Recommended Citation
F. Montesino Pouzols and A. Lendasse, "Evolving Fuzzy Optimally Pruned Extreme Learning Machine: A Comparative Analysis," 2010 IEEE World Congress on Computational Intelligence, WCCI 2010, article no. 5584327, Institute of Electrical and Electronics Engineers, Nov 2010.
The definitive version is available at https://doi.org/10.1109/FUZZY.2010.5584327
Department(s)
Engineering Management and Systems Engineering
International Standard Book Number (ISBN)
978-142446920-8
Document Type
Article - Conference proceedings
Document Version
Citation
File Type
text
Language(s)
English
Rights
© 2024 Institute of Electrical and Electronics Engineers, All rights reserved.
Publication Date
25 Nov 2010
