Referenced Items are not available for this document.
This paper deals with a simplified version of the evolving Takagi-Sugeno (eTS) learning algorithm - a computationally efficient procedure for on-line learning TS type fuzzy models. It combines the concept of the scatter as a measure of data density and summarization ability of the TS rules, the use of Cauchy type antecedent membership functions, an aging indicator characterizing the stationarity of the rules, and a recursive least square algorithm to dynamically learn the structure and parameters of the eTS model
Published in:
Fuzzy Systems, 2005. FUZZ '05. The 14th IEEE International Conference on
Date of Conference: 25-25 May 2005
- Page(s):
- 1068 - 1073
- Print ISBN:
- 0-7803-9159-4
- INSPEC Accession Number:
- 8615481
- Conference Location :
- Reno, NV
- Digital Object Identifier :
- 10.1109/FUZZY.2005.1452543
- Product Type:
- Conference Publications
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