Identification of possibilistic linear systems by quadratic membership functions of fuzzy parameters

We have already discussed several models of the possibilities linear regression analysis under the assumption that probabilitioes parameters in the models are non-interactive, i.e., the possibilistic distribution of parameters is defined by minimum operators. In this paper, quadratic membership func...

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Bibliographic Details
Published in:Fuzzy sets and systems 1991-05, Vol.41 (2), p.145-160
Main Authors: Tanaka, H., Ishibuchi, H.
Format: Article
Language:English
Subjects:
Exact sciences and technology
Fuzzy regression analysis
Linear inference, regression
Mathematics
possibilistic regression analysis
Probability and statistics
quadratic membership function
Sciences and techniques of general use
Statistics
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Summary:We have already discussed several models of the possibilities linear regression analysis under the assumption that probabilitioes parameters in the models are non-interactive, i.e., the possibilistic distribution of parameters is defined by minimum operators. In this paper, quadratic membership functions as defined by A. Celmiņš are considered to propose an identification method of interactive fuzzy parameters in possibilistic linear systems. Our method can be reduced to linear programming, so that it is very easy to obtain the posssibilistic distribution of parameters.
ISSN:0165-0114
1872-6801
DOI:10.1016/0165-0114(91)90218-F