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 :
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