Hierarchical Fuzzy identification using gradient descent and recursive least square method

Zeinab Fallah, Mojtaba Aahmadieh Khanesar, Mohammad Teshnehlab

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Abstract

In this paper, the parameters of hierarchical fuzzy systems are trained using the simultaneous use of Gradient Descent (GD) for nonlinear parameters and recursive least square (RLS) algorithm for linear parameters. One of the most effective ways to overcome the curse of dimensionality of fuzzy systems is the use of hierarchical fuzzy systems (HFS). Considering the learning abilities of fuzzy systems, two learning algorithms GD and GD+RLS have been used to teach HFS. The results of simulation show that, the use of HFS causes the decrease in the number of rules and results in better performance in identification. In addition, when GD+RLS algorithm is used for learning HFS, it produces better results when it is compared to GD algorithm.

Original languageEnglish
Title of host publication2013 3rd IEEE International Conference on Computer, Control and Communication, IC4 2013
Publication date2013
Article number6653750
ISBN (Print)9781467358859
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 3rd IEEE International Conference on Computer, Control and Communication, IC4 2013 - Karachi, Pakistan
Duration: 25 Sep 201326 Sep 2013

Conference

Conference2013 3rd IEEE International Conference on Computer, Control and Communication, IC4 2013
CountryPakistan
CityKarachi
Period25/09/201326/09/2013

Keywords

  • Gradient Descent
  • Hierarchical Fuzzy Systems
  • Recursive Least Square

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