Stochastic Resonance Effect in Optimal Decision Solution Under Neyman–Pearson Criterion-Reference-Cited by-同舟云学术

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Stochastic Resonance Effect in Optimal Decision Solution Under Neyman–Pearson Criterion

Published:2021-01-13 Issue:7 Volume:40 Page:3286-3304
ISSN:0278-081X
Container-title:Circuits, Systems, and Signal Processing
language:en
Short-container-title:Circuits Syst Signal Process

Author:

Yang Ting^ORCID,Li Yu,Yang Shiju,Liu Shujun

Funder

National Natural Science Foundation of China

Chongqing Municipal Education Commission

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Signal Processing

Link

https://link.springer.com/content/pdf/10.1007/s00034-020-01644-y.pdf

Reference32 articles.

1. A.B. Akbay, S. Gezici, Noise benefits in joint detection and estimation problems. Signal Process. 118, 235–247 (2016)

2. K. Audhkhasi, O. Osoba, B. Kosko, Noise-enhanced convolutional neural networks. Neural Netw. 78, 15–23 (2016)

3. G.O. Balkan, S. Gezici, CRLB based optimal noise enhanced parameter estimation using quantized observations. IEEE Signal Process. Lett. 17(5), 477–480 (2010)

4. S. Bayram, S. Gezici, Noise enhanced M-ary composite hypothesis-testing in the presence of partial prior information. IEEE Trans. Signal Process. 59(3), 1292–1297 (2011)

5. S. Bayram, S. Gezici, H.V. Poor, Noise enhanced hypothesis-testing in the restricted Bayesian framework. IEEE Trans. Signal Process. 58(8), 3972–3989 (2010)

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