Improving fuzzy systems identification with data transformations

Armin Shmilovici, Joseph Aguilar-Martin

Research output: Contribution to journalArticlepeer-review

7 Scopus citations

Abstract

A practical problem in the identification of fuzzy systems from data, is the design and the tuning of the membership functions. We demonstrate that if the data is properly transformed before the identification process, the resulting fuzzy model can be improved to the point it may not need a further tuning. The significance of the data transform can be validated using statistical methods. The method is demonstrated on a time series prediction problem, using the Box-Cox transform.

Original languageEnglish
Pages (from-to)93-107
Number of pages15
JournalInternational Journal of Approximate Reasoning
Volume22
Issue number1
DOIs
StatePublished - 1 Jan 1999

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