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基于随机共振和人工鱼群算法的微弱信号智能检测系统 被引量:22

Weak signal intelligent detection system based on stochastic resonance and artificial fish swarm algorithm
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摘要 信噪比的提高是微弱信号检测的关键,实际工程信号只有与非线性双稳系统满足匹配条件,才能产生随机共振,提高信噪比。以LabVIEW为软件开发平台,以工控机和NI数据采集卡为硬件平台,利用LabVIEW与MATLAB混合编程研制了基于随机共振和人工鱼群算法的微弱信号智能检测系统。该检测系统能够根据被检测信号的特性,以信噪比为评价函数,利用人工鱼群算法自适应地调节双稳系统参数,从而实现随机共振的产生和增强。检测过程中的波形可图形化显示,人机界面友好。经对涡街信号的检测表明系统能有效地实现微弱特征信号的检测。 The improvement of signal-to-noise ratio (SNR) is the key to weak signal detection. Only when the input signal in engineering practice satisfies the condition matching with nonlinear bitable system, the stochastic resonance can be generated and then the SNR is improved. Taking LabVIEW as the software development plat- form and an industrial PC and an NI data acquisition card as the hardware platform, a weak signal intelligent de- tection system based on stochastic resonance and artificial fish swarm algorithm was developed using the mixed programming with LabVIEW and MATLAB. According to the characteristics of the signal to be detected, taking SNR as the evaluation function, the detection system can adaptively adjust the parameters of the bistable system using the artificial fish swarm algorithm. Thus the generation and enhancement of the stochastic resonance are re- alized. The waveform can be displayed graphically in the detection process, and the system has friendly man-machine interface. The detection of the signal of a vortex flowmeter shows that the system can realize weak characteristic signal detection effectively.
作者 朱维娜 林敏
出处 《仪器仪表学报》 EI CAS CSCD 北大核心 2013年第11期2464-2470,共7页 Chinese Journal of Scientific Instrument
基金 国家自然科学基金(10972207) 浙江省自然科学基金(LY13A020004,LY13E050012)资助项目
关键词 随机共振 信噪比 人工鱼群算法 LabVIEW与MATLAB 智能检测 stochastic resonance signal-to-noise ratio (SNR) artificial fish swarm algorithm LabVIEW andMATLAB intelligent detection
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  • 1夏均忠,刘远宏,冷永刚,葛纪桃.微弱信号检测方法的现状分析[J].噪声与振动控制,2011,31(3):156-161. 被引量:81
  • 2MENG X,DODSON A H,ROBERTS G W.Detecting bridge dynamics with GPS and triaxial accelerometers[J].Engineering Structures,2007,29(11):3178-3184.
  • 3郭晓宇,刘敬彪,刘纯虎.基于酶电极的有机磷农药检测仪[J].仪表技术与传感器,2009(2):25-28. 被引量:3
  • 4BENZI R,SUTERA A,VULPINAI A.The mechanism of stochastic resonance[J].Journal of Physics A:Mathematical and General,1981,14(11):453-457.
  • 5MITAIM S,KOSKO B.Adaptive stochastic resonance[J].Proceedings of the IEEE,1998,86(11):2152-2183.
  • 6林敏,黄咏梅,方利民.双稳系统随机共振的反馈控制[J].物理学报,2008,57(4):2041-2047. 被引量:11
  • 7LI J L,XU B H.Effects of signal spectrum varying on signal processing by parameter-induced stochastic resonance[J].Physica A:Statistical Mechanics and its Apphcations,2006,361(1):11-23.
  • 8李晓磊,邵之江,钱积新.一种基于动物自治体的寻优模式:鱼群算法[J].系统工程理论与实践,2002,22(11):32-38. 被引量:884
  • 9BERNARDINO E M,BERNARNO A M,SANCHES-PEREZ J M,et al.Swarm optimisation algorithms applied to large balanced communication networks[J].Journal of Network and Computer Applications,2013,36 (1):504-522.
  • 10SHEN W,GUO X P,WU D S.Forecasting stock indices using radial function neural networks optimized by artificial fish swarm algorithm[J].Knowledge-Based Systems,2011,24(3):378-385.

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