Stochastic resonance benefits in signal detection under MAP criterion

DOI10.1016/J.CNSNS.2021.105919zbMath1470.94061OpenAlexW3168040516MaRDI QIDQ2045966

Yu Li, Hongqing Liu, Shiju Yang, Ting Yang, Shu-Jun Liu

Publication date: 16 August 2021

Published in: Communications in Nonlinear Science and Numerical Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.cnsns.2021.105919

zbMATH Keywords

nonlinear systemhypothesis testingstochastic resonanceMAP criterion

Mathematics Subject Classification ID

Parametric hypothesis testing (62F03) Signal theory (characterization, reconstruction, filtering, etc.) (94A12) Detection theory in information and communication theory (94A13)

Cites Work

• Differential evolution. A practical approach to global optimization. With CD-ROM.
• Stochastic resonance in a delay-controlled dissipative bistable potential for weak signal enhancement
• Stochastic resonance in dissipative drift motion
• Noise-enhanced convolutional neural networks
• Theory of the Stochastic Resonance Effect in Signal Detection: Part I—Fixed Detectors
• Theory of the Stochastic Resonance Effect in Signal Detection—Part II: Variable Detectors
• Noise Enhanced Hypothesis-Testing in the Restricted Bayesian Framework
• Optimal Noise Benefits in Neyman–Pearson and Inequality-Constrained Statistical Signal Detection
• Noise Enhanced <formula formulatype="inline"><tex Notation="TeX">$M$</tex></formula>-ary Composite Hypothesis-Testing in the Presence of Partial Prior Information
• MMSE Approximation For Sparse Coding Algorithms Using Stochastic Resonance

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