Skip to main content
padlock icon - secure page this page is secure

On-Line Identification of Takagi-Sugeno Model Based on Improved Density-Based Clustering Algorithm

Buy Article:

$106.34 + tax (Refund Policy)

For the complexity and time-consuming of the density-based clustering algorithm (DBSCAN), an improved DBSCAN algorithm is proposed to simplify the clustering, and on this basis present a new algorithm for on-line identification of Takagi-Sugeno (TS) fuzzy model. The new on-line clustering based on improved DBSCAN algorithm is adopted in structure identification, rules can be added, modified and deleted dynamically to reflect the influence of the new arriving data timely, and then update consequent parameters according to the number of rules, finally the recursive least square method is applied to identify the consequent parameters. The approach has been tested on data from Box-Jenkins gas furnace and a second-order nonlinear uncertain system, and the results show the viability and effectiveness of the approach.
No Reference information available - sign in for access.
No Citation information available - sign in for access.
No Supplementary Data.
No Article Media
No Metrics


Document Type: Research Article

Publication date: March 1, 2012

More about this publication?
  • ADVANCED SCIENCE LETTERS is an international peer-reviewed journal with a very wide-ranging coverage, consolidates research activities in all areas of (1) Physical Sciences, (2) Biological Sciences, (3) Mathematical Sciences, (4) Engineering, (5) Computer and Information Sciences, and (6) Geosciences to publish original short communications, full research papers and timely brief (mini) reviews with authors photo and biography encompassing the basic and applied research and current developments in educational aspects of these scientific areas.
  • Editorial Board
  • Information for Authors
  • Subscribe to this Title
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more