摘要
针对传统随机共振方法检测微弱信号精度低的问题,把全局版人工鱼群算法与随机共振算法相结合,提出了一种基于全局人工鱼群算法的自适应随机共振微弱信号检测方法。该方法把随机共振输出信噪比增益作为全局人工鱼群算法的适应度函数,将微弱信号检测问题转化为多参数并行寻优问题。分别在Langevin和Duffing系统中进行仿真实验及其寻优结果对比研究,仿真结果表明本文所提方法高效可行,相比较Langevin系统,Duffing自适应随机共振系统体现更高的微弱信号检测精度和检测性能。引入控制频率,将Duffing自适应随机共振应用于多频大信号的检测,拓宽了随机共振的应用范围。
As traditional stochastic resonance methodhas low accuracy problem for weak signal detection,a new adaptive stochastic resonanceweak signal detection method based on global artificial fish swarm algorithm （GAFSA） is proposed, in which the global artificial fish swarm algorithm is combined with stochastic resonance. The output signal-to-noise ratio gain of stochastic resonance system is used as fitness function of the global artificialfish swarm algorithm, and the weak signal detection problem is transformed into multi-parameter parallel optimization problem, which formsan adaptive system to detect weak feature signal. The simulation experiments and the comparisons of optimization results are carried out respectively in the Langevin and Duffing systems. Simulation results show thatthe proposed method is efficient and feasible. Compared to Langevin system, DutYmg adaptive stochastic resonance system has higher detection accuracy and performance of the weak signal detection. Duffing optimal system is applied to the multi-frequency large signal detection, which widens the application range of stochastic resonance.
出处
《系统仿真学报》
CAS
CSCD
北大核心
2018年第2期587-594,604,共9页
Journal of System Simulation
基金
国家自然科学基金(61671248),江苏省高校自然科学研究重大项目(15KJA460008)
关键词
自适应
随机共振
DUFFING振子
全局人工鱼群算法
微弱信号检测
self-adaption
stochastic resonance
Duffing oscillator
global artificial fish swarm algorithm
weak signal detection
