随机共振
双稳态
信号(编程语言)
控制理论(社会学)
噪音(视频)
信号传递函数
共振(粒子物理)
探测理论
统计物理学
功能(生物学)
航程(航空)
计算机科学
生物系统
物理
核磁共振
人工智能
量子力学
生物
电信
探测器
图像(数学)
程序设计语言
作者
Yi Wang,Jiao Shangbin,Qinghua Zhang,Shuang Lei,Xiaoxue Qiao
标识
DOI:10.1016/j.cjph.2018.04.002
摘要
Abstract Aiming at detecting the weak signal in a strong noise background, an enhanced weak signal detection method based on adaptive parameter-induced tri-stable stochastic resonance is proposed. Firstly, because the system can switch among the monostable, bistable and tri-stable state, the potential function characteristic of tri-stable systems is studied by analyzing the potential function curves with different system parameters. And the dynamic characteristics of system parameters on the depth of the potential well is analyzed. The ranges of R and the system parameters are determined, which is essential for ensuring the system is tri-stable state. Secondly, the range of R is used as the constraint condition and the average output signal-to-noise ratio is used as the fitness function of the adaptive algorithm. The system parameters a, b, c are optimized by the differential evolution particle swarm optimization (DEPSO) method to obtain the best output effect. Finally, the proposed adaptive parameter-induced tri-stable stochastic resonance method is adopted to detect the mixed multiple high-frequency weak signal. The detection results are compared with that of adaptive bistable stochastic resonance. At the meanwhile, the method is also applied to detect the fault signal of single crystal furnace. Both the simulation analysis and experiment results show that the proposed method can effectively improve the output signal-to-noise ratio and detect multi-frequency weak signal in the strong noise background.
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