Study on Stochastic Resonance Algorithm Based on Bayes Criterion

LIU Shujun; YANG Ting; TANG Mingchun; WANG Pin; LI Yongming

doi:10.11999/JEIT160361

Volume 39 Issue 2

Feb. 2017

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Article Navigation > Journal of Electronics & Information Technology > 2017 > 39(2): 293-300

LIU Shujun, YANG Ting, TANG Mingchun, WANG Pin, LI Yongming. Study on Stochastic Resonance Algorithm Based on Bayes Criterion[J]. Journal of Electronics & Information Technology, 2017, 39(2): 293-300. doi: 10.11999/JEIT160361

Citation:

LIU Shujun, YANG Ting, TANG Mingchun, WANG Pin, LI Yongming. Study on Stochastic Resonance Algorithm Based on Bayes Criterion[J]. Journal of Electronics & Information Technology, 2017, 39(2): 293-300. doi: 10.11999/JEIT160361

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Study on Stochastic Resonance Algorithm Based on Bayes Criterion

doi: 10.11999/JEIT160361

Funds:

The Basic and Advanced Research Project in Chongqing (cstc2016jcyjA0134, cstc2016jcyjA0043), The National Natural Science Foundation of China (61501072, 61301224, 41404027, 61108086, 61471072), The Chongqing Social Undertaking and People,s Livelihood Guarantee Science and Technology Innovation Special Foundation (cstc2016shmszx40002), The Fundamental Research Funds for the Central Universities (CDJZR155507)

Received Date: 2016-04-14
Rev Recd Date: 2016-10-18
Publish Date: 2017-02-19

Abstract

Abstract

The optimal noise that minimizes Bayes risk for a binary hypothesis testing problem is analyzed firstly. As a result, the minimization of Bayes risk can be equivalent as the optimization of the detection probability and/or false alarm probability . Secondly, a noise enhanced model, which can increase and decrease simultaneously, is established under the premise of maintaining predefined and . Then the optimal additional noise of this model is obtained by a convex combination of two optimal noises of two limit cases, which are the minimization of with maintaining the predefined and the maximization of with maintaining the predefined , respectively. Furthermore, the sufficient conditions for this model are given. Whats more, the additive noise that minimizes the Bayes risk is determined when the prior probabilities are known or not, and the corresponding additive noise can be obtained by recalculating a parameter only if the prior information changes. Finally, the availability of algorithm is proved through the simulation combined with a specific detection example.
- Signal processing,
- Bayes criterion,
- Noise enhanced model,
- Additive noise,
- Hypothesis testing

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References(24)

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