A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential model
A novel parameter-induced adaptive stochastic resonance system based on composite multi-stable potential model
复制标题
基于复合多稳态势模型的新型参数诱导自适应随机共振系统
DOI：
10.1016/j.cjph.2019.02.031
复制
发表时间：
2019-06-01
影响因子：
5
通讯作者：
Jiang Wei
中科院分区：
文献类型：
--
作者：
Jiao Shangbin;Qiao Xiaoxue;Jiang Wei
随机共振（SR）作为一种利用噪声进行微弱信号检测的方法，在许多领域得到了广泛的应用。为了提高超随机共振的弱信号处理能力，提出了一种新的复合多稳态模型，该模型由三稳态模型和高斯势（GP）模型联合构成。基于该模型构建了超分辨率成像系统，并以信噪比作为衡量超分辨率成像效果的指标。采用差分脑风暴优化算法（DBSO）协同优化系统参数，实现参数诱导的自适应SR。分析了高斯白色噪声和稳态噪声环境下系统参数V、R和噪声强度D对SR系统输出响应的影响，验证了复合多稳态SR系统相对于传统三稳态系统的优越性。针对不同电平的微弱信号，比较分析了基于复合多稳态模型、传统三稳态模型、复合三稳态模型的SR系统的输出性能。结果证明该模型具有更好的性能。同时，利用复合多稳态SR系统实现了对多个高频微弱信号的自适应检测。仿真结果表明，该系统具有较强的弱信号处理能力和较好的抗噪声能力，拓宽了随机共振在实际工程中的应用范围。
Stochastic resonance (SR) is used widely as a weak signal detection method by using noise in many fields. In order to improve the weak signal processing capability of SR, a novel composite multi-stable model is proposed, which is constructed by the joint of the tristable model and the Gaussian Potential (GP) model. The SR system based on this model is constructed and the signalto-noise ratio (SNR) is regarded as the index to measure the SR effect. The differential brain storm optimization (DBSO) algorithm is used to optimize the system parameters collaboratively to achieve parameter-induced adaptive SR. The influences of the system parameters V and R and the noise intensity D on the output response of SR system are analyzed under Gaussian white noise and a stable noise environments, and the advantages of the composite multi-stable SR system over the traditional tristable system are verified. For different levels of weak signals, the output performances of SR systems based on composite multi-stable model, traditional tristable model, composite tristable model are compared and analyzed. The results prove that the proposed model has better performance. Meanwhile, the adaptive detection of the multiple high-frequency weak signal is realized using the composite multi-stable SR system. The simulation results show that the proposed system has strong weak signal processing capability and good immunity to noise types, which widens the application range of SR in practical engineering.