Abstract
The use of fuzzy sets in representing uncertainty in signal detection and estimation problems has been shown to complement the conventional approaches using probabilistic modeling. We concentrate on an approach, where the sample information available from the physical phenomenon of interest is assumed to be vague. We study and analyze a parameter estimation scheme in a decentralized system when the data available at each sensor is vague. The vagueness of the data is represented by means of `fuzzy events' defined over the real line. The optimum global estimator in a minimum mean square error sense is obtained, and the corresponding optimum partitioning of the fuzzy information space is presented. We also discuss a suboptimum data partitioning method using the Fisher information measure.
Original language | English (US) |
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Title of host publication | Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS |
Publisher | IEEE Computer Society |
Pages | 142-147 |
Number of pages | 6 |
State | Published - 1997 |
Event | Proceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'97 - Syracuse, NY, USA Duration: Sep 21 1997 → Sep 24 1997 |
Other
Other | Proceedings of the 1997 Annual Meeting of the North American Fuzzy Information Processing Society, NAFIPS'97 |
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City | Syracuse, NY, USA |
Period | 9/21/97 → 9/24/97 |
ASJC Scopus subject areas
- General Computer Science
- Media Technology