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Identification of key variables using fuzzy average with fuzzy cluster distribution.

Authors
Hou-Y; Zurada-JM; Karwowski-W; Marras-WS; Davis-K
Source
IEEE Trans Fuzzy Syst 2007 15(4):673-685
NIOSHTIC No.
20041175
Abstract
Identification of the significance of input variables is very important for complex systems with high-dimensional input space. In this paper, a method using fuzzy average with fuzzy cluster distribution is proposed. To avoid the interference of different distributions of the sampling data, the distribution of fuzzy clusters in the sampling data is considered, instead of the original data set. To discover the input-output relationship, the methods of fuzzy rules and fuzzy C-means are first used to partition the original sampling data set into fuzzy clusters. A new data set with the same distribution of the fuzzy clusters is produced. The fuzzy average method is then applied to the new data set. By doing so, the interference of distribution of the original sampling data is removed. This method is straightforward and computationally easy. The performance is tested on both benchmark data and real-world data.
Keywords
Models; Mathematical-models; Computer-models; Computer-software; Statistical-analysis; Author Keywords: Fuzzy cluster; variable identification
CODEN
IEFSEV
Publication Date
20070801
Document Type
Journal Article
Email Address
karwowski@louisville.edu
Funding Type
Grant
Fiscal Year
2007
NTIS Accession No.
NTIS Price
Identifying No.
Grant-Number-R01-OH-007787
Issue of Publication
4
ISSN
1063-6706
Source Name
IEEE Transactions on Fuzzy Systems
State
OH; KY
Performing Organization
Ohio State University
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