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为提升随机共振理论在微弱信号检测领域中的实用性,以随机共振系统参数为研究对象,提出了基于量子粒子群算法的自适应随机共振方法.首先将自适应随机共振问题转化为多参数并行寻优问题,然后分别在Langevin系统和Duffing振子系统下进行仿真实验.在Langevin系统中,将量子粒子群算法和描点法进行了寻优结果对比;在Duffing振子系统中,Duffing振子系统的寻优结果则直接与Langevin系统的寻优结果进行了对比.实验结果表明:在寻优结果和寻优效率上,基于量子粒子群算法的自适应随机共振方法要明显高于描点法;在相同条件下,Duffing振子系统的寻优结果要优于Langevin系统的寻优结果;在两种系统下,输入信号信噪比越低就越能体现出量子粒子群算法的优越性.最后还对随机共振系统参数的寻优结果进行了规律性总结.
In order to improve the practicability of stochastic resonance theory in the field of weak signal detection, an adaptive stochastic resonance method based on quantum particle swarm optimization is put forward based on stochastic resonance system parameters.Firstly, the problem of adaptive stochastic resonance is transformed into multiparameter parallel And then the simulation is carried out under Langevin system and Duffing oscillator system respectively.In contrast with the Langevin system, the quantum particle swarm optimization algorithm and the spot-point method are compared. In the Duffing oscillator system, the optimization of the Duffing oscillator system The results are directly compared with the Langevin system optimization results.The experimental results show that the adaptive stochastic resonance method based on quantum particle swarm optimization algorithm is significantly higher than the punctiform method in the optimization results and optimization efficiency; under the same conditions , The optimal results of Duffing oscillator system are better than that of Langevin system.Under the two systems, the lower the signal-to-noise ratio of input signal, the better the performance of quantum particle swarm optimization.Finally, The optimization results of the parameters are summarized regularly.