DocumentCode
1022126
Title
An application of the generalized Neyman-Pearson fuzzy test to stochastic-signal detection
Author
Son, Jae Cheol ; Song, Iickho ; Kim, Sun Yong ; Park, Seong III
Author_Institution
Dept. of Electr. Eng., Korea Adv. Inst. of Sci. & Technol., Daejon, South Korea
Volume
23
Issue
5
fYear
1993
Firstpage
1474
Lastpage
1481
Abstract
In the article an application of the fuzzy testing of hypothesis to the stochastic-signal detection problem is considered when the signal-to-noise ratio approaches zero. We first obtain the general relationship between the test statistic of the locally optimum fuzzy detector and that of the locally optimum detector. Based on this result, the test statistic and structures of the locally optimum fuzzy detector for stochastic signals are obtained. Several aspects of the locally optimum fuzzy nonlinearity for stochastic signals are also described. Finally, performance characteristics of the locally optimum fuzzy detector are briefly discussed
Keywords
fuzzy set theory; signal detection; statistical analysis; generalized Neyman-Pearson fuzzy test; locally optimum fuzzy detector; locally optimum fuzzy nonlinearity; signal-to-noise ratio; stochastic-signal detection; test statistic; Automatic control; Detectors; Motion control; Path planning; Quantization; Robotics and automation; Signal detection; Statistical analysis; Stochastic processes; Testing;
fLanguage
English
Journal_Title
Systems, Man and Cybernetics, IEEE Transactions on
Publisher
ieee
ISSN
0018-9472
Type
jour
DOI
10.1109/21.260679
Filename
260679
Link To Document