基于人工鱼群算法的自适应随机共振方法研究

doi:10.3969/j.issn.0372-2112.2017.08.009

电子学报 ›› 2017, Vol. 45 ›› Issue (8): 1864-1872.DOI: 10.3969/j.issn.0372-2112.2017.08.009

基于人工鱼群算法的自适应随机共振方法研究

孔德阳, 彭华, 马金全

解放军信息工程大学信息系统工程学院, 河南郑州 450002

收稿日期:2016-05-23 修回日期:2016-11-21 出版日期:2017-08-25
- 通讯作者:
- 孔德阳
- 作者简介:
- 彭华,男,1976出生,江西萍乡人,解放军信息工程大学教授、博士生导师,主要研究方向为软件无线电、通信信号处理等.E-mail:pengh139@139.com;马金全,男,1975出生,甘肃天水人,解放军信息工程大学副教授,主要研究方向为软件无线电、信号逆向处理与分析等.
- 基金资助:
- 国家自然科学基金 (No.61401511）

Adaptive Stochastic Resonance Method Based on Artificial-fish Swarm Optimization

KONG De-yang, PENG Hua, MA Jin-quan

PLA Information Engineering University, Zhengzhou, Henan 450002, China

Received:2016-05-23 Revised:2016-11-21 Online:2017-08-25 Published:2017-08-25

摘要/Abstract

摘要： 随机共振为微弱通信信号的检测提供了新途径.本文提出一种基于人工鱼群算法的自适应随机共振新方法，重点研究基于随机共振的微弱周期信号检测技术，将人工鱼群算法和归一化处理结合增强随机共振，适当添加噪声并设定自适应步长策略及迭代停止条件.理论分析和仿真结果表明，对比传统群智能算法处理随机共振其在算法适应性及稳定性、最佳共振精确度、寻优收敛速度、精度方面有明显提升，并为信噪比增益带来3-5dB的提升，运算时间复杂度降低逾70%.

关键词: 随机共振, 人工鱼群算法, 归一化处理, 自适应步长

Abstract: Stochastic resonance provided new ways detecting weak communication signals.A new method based on artificial-fish swarm optimization is proposed whose emphasis is on weak periodic signal detection based on stochastic resonance.Artificial-fish swarm optimization is combined with normalization of stochastic resonance.Noise is utilized by adding it to the signal while new adaptive step strategy and iteration stopping strategy are also used.The results of theoretical analysis and simulation experiments show this proposed method have great promotions on adaptability,stability,precision of the optimal resonance and the convergence speed,as well as a great promotion of 3-5dB on SNR gain compared with traditional stochastic resonance based on swarm optimization.Meanwhile,the time complexity of calculation is decreased by 70%.

Key words: stochastic resonance, artificial-fish swarm optimization, normalization method, adaptive step

中图分类号:

TN911.7

孔德阳, 彭华, 马金全. 基于人工鱼群算法的自适应随机共振方法研究[J]. 电子学报, 2017, 45(8): 1864-1872.

KONG De-yang, PENG Hua, MA Jin-quan. Adaptive Stochastic Resonance Method Based on Artificial-fish Swarm Optimization[J]. Acta Electronica Sinica, 2017, 45(8): 1864-1872.

参考文献

[1] Mitaim S,Kosko B.Adaptive stochastic resonance[J].Proceedings of the IEEE, 1998, 86(11):2152-2183.
[2] 高锐,李赞,吴利平等.低信噪比条件下基于随机共振的感知方法与性能分析[J].电子学报, 2013, 41(9):1672-1679. Gao Rui, Li Zan, Wu Liping, et al.A spectrum sensing method and performance analysis based on stochastic resonance under low SNR[J].Acta Electronica Sinica, 2013, 41(9):1672-1679.(in Chinese)
[3] Fengyuan.Detection of cycle signal and non-periodic signal based on the principle of stochastic resonance[J].Computing Technology and Automation, 2016, 35(1):86-88.
[4] Han Dongying, Ding Xuejuan, Shi Peiming.Multi-frequency weak signal detection based on EMD after de-noising by adaptive re-scaling frequency-shifted band-pass stochastic resonance[J].Journal of Mechanical Engineering, 2014, 49(8):10-18.
[5] Jun Wang, Xin Ren, Shaosen Zhang.Adaptive bistable stochastic resonance aided spectrum sensing[J].IEEE Transactions on Wireless Communications, 2014,13(7):4014-4024.
[6] 马卫,孙正兴.采用搜索趋化策略的布谷鸟全局优化算法[J].电子学报, 2015, 43(12):2429-2439. Ma Wei, Sun Zhengxing.A global cuckoo optimization algorithm using coarse-to-fine search[J].Acta Electronica Sinica, 2015, 43(12):2429-2439.(in Chinese)
[7] Zhang Gang, Hu Tao.Adaptive stochastic resonance system based on frequency control[J].Journal of Vibration and Shock, 2016, 35(2):91-96.
[8] 李一博,张博林等.基于量子粒子群算法的自适应随机共振方法研究[J].物理学报, 2014, 63(16):40-47. Li Yibo, Zhang Bolin, Liu Zixin, et al.Adaptive stochastic resonance method based on quantum particle swarm optimization[J].Acta Physica Sinica, 2014, 63(16):40-47.(in Chinese)
[9] An Yisheng, Yuan Shaoxin, Zhao Xiangmo.Optimization of dynamic route choice based on ant colony algorithm[J].Journal of Transportation Systems Engineering and Information Technology, 2014, 11(3):97-103.
[10] Zhu Weina, Lin Min.Weak signal intelligent detection system based on stochastic resonance and artificial fish swarm algorithm[J].Chinese Journal of Scientific Instrument, 2013, 34(11):2464-2470.
[11] Gan Yan, Wei Yan, Yang You, et al.Improved random moved function based AFSA[J].Computer Engineering and Applications, 2014, 50(13):147-152.
[12] 马宪民,刘妮.自适应视野的人工鱼群算法求解最短路径问题[J].通信学报,2014, 35(1):1-6. Ma Xianmin, Liu Ni.Improved artificial fish-swarm algorithm based on adaptive vision for solving the shortest path problem[J].Journal on Communication, 2014, 35(1):1-6.(in Chinese)
[13] 杨定新,胡政等.大参数周期信号随机共振解析[J].物理学报, 2012,61(8):50-59. Yang Dingxin, Hu Zheng, et al.The analysis of stochastic resonance of periodic signal with large parameters[J].Acta Physica Sinica, 2012,61(8):50-59.(in Chinese)
[14] 冷永刚,王太勇,郭焱.双稳随机共振参数特性的研究[J].物理学报, 2007, 56(1):30-36. Leng Yonggang, Wang Taiyong, Guo Yan.Study of the property of the parameters of bi-stable stochastic resonance[J].Acta Physica Sinica, 2007, 56(1):30-36.(in Chinese)
[15] 冷永刚.基于Kramers逃逸速率的调参随机共振机理[J].物理学报, 2009, 58(8):5196-5200. Leng Yonggang.Mechanism of parameter-adjusted stochastic resonance based on Kramers rate[J].Acta Physica Sinica, 2009, 58(8):5196-5200.(in Chinese)
[16] McNamara B, Wiesenfeld K.Theory of stochastic resonance[J].Rev Mod Phys, 1998, 70(1):223-287.

基于人工鱼群算法的自适应随机共振方法研究

Adaptive Stochastic Resonance Method Based on Artificial-fish Swarm Optimization

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