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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 enhance the usefulness of the theory of stochastic resonance in the areas of weak signal detection, a new method based on quantum particle swarm optimization is proposed to conquer with the problem of adaptive stochastic resonance. First, the problem of adaptive stochastic resonance is converted into the problem of multi-parameter optimization. Then simulation experiments are conducted respectively under a Langevin system and Duffing oscillator system. At the same time, Point detection method is chosen as the comparative test in the Langevin system. While in the Duffing system, the optimization results are compared with those from the Langevin system directly. Results show that the method based on quantum particle swarm optimization is obviously superior to the point detection method and optimization result in the Duffing oscillator is better than that from Langevin system under the same condition. Besides, it is also found that the lower the SNR of input signal, the more effective the quantum particle swarm optimization is. Finally, the regularity of optimization results of the stochastic resonance system parameters is summarized.
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Keywords:
- adaptive stochastic resonance /
- quantum particle swarm optimization /
- multi-parameter optimization
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[1] Benzi R, Parisi G, Vulpiani A 1983 SIAM J. Appl. Math. 43 565
[2] Qin G R, Gong D C, Hu G, Wen X D 1992 Acta Phys. Sin. 41 360 (in Chinese) [秦光戎, 龚德纯, 胡岗, 温孝东 1992 41 360]
[3] Zhu G Q, Ding K, Zhang Y, Zhao Y 2010 Acta Phys. Sin. 59 300 (in Chinese) [朱光起, 丁珂, 张宇, 赵远 2010 59 300]
[4] Gao S L, Zhong S C, Wei K, Ma H 2012 Acta Phys. Sin. 61 100502 (in Chinese) [高仕龙, 钟苏川, 韦鹍, 马洪 2012 61 100502]
[5] Ye Q H, Huang H N, Zhang C H 2009 Acta Electron. Sin. 37 216 (in Chinese) [叶青华, 黄海宁, 张春华 2009 电子学报 37 216]
[6] Wang L Y, Yin C S, Cai W S, Pan Z X 2001 Chem. J. Chin. Univ. 22 762 (in Chinese) [王利亚, 印春生, 蔡文生, 潘忠孝 2001 高等学校化学学报 22 762]
[7] Zhao Y J, Wang T Y, Leng Y G, Xu Y, Zhang P 2009 J. Tianjin Univ. 42 123 (in Chinese) [赵艳菊, 王太勇, 冷永刚, 徐跃, 张攀 2009 天津大学学报 42 123]
[8] Kang Y M, Xu J X, Xie Y 2004 Acta Mech. Sin. 36 247 (in Chinese) [康艳梅, 徐健学, 谢勇 2004 力学学报 36 247]
[9] Leng Y G, Lai Z H 2014 Acta Phys. Sin. 63 020502 (in Chinese) [冷永刚, 赖志慧 2014 63 020502]
[10] Li Q, Wang T Y, Leng Y G, He G Y, He H L 2007 Acta Phys. Sin. 56 6803 (in Chinese) [李强, 王太勇, 冷永刚, 何改云, 何慧龙 2007 56 6803]
[11] Huang Z X, Yu Y H, Huang D C 2012 J. Shanghai Jiaotong. Univ. 46 228 (in Chinese) [黄泽霞, 俞攸红, 黄德才 2012 上海交通大学学报 46 228]
[12] Zhang Z S 2010 Expert Syst. Appl. 37 1800
[13] Zhang H L, Song L L 2013 Acta Phys. Sin. 62 190508 (in Chinese) [张宏立, 宋莉莉 2013 62 190508]
[14] Guo Y C, Hu L L, Ding R 2012 Acta Phys. Sin. 61 054304 (in Chinese) [郭业才, 胡苓苓, 丁锐 2012 61 054304]
[15] Gao F, Li Z Q, Tong H Q 2008 Chin. Phys. B 17 1196
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