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Parameters estimate of recurrent quantum stochastic filter for time variant frequency periodic signals

时变频率周期信号的递归量子随机滤波器参数估计

Abstract

Designing optimal time and spatial difference step size is the key technology for quantum-random filtering (QSF) to realize time-varying frequency periodic signal filtering. In this paper, it was proposed to use the short-time Fourier transform (STFT) to dynamically estimate the signal to noise ratio (SNR) and relative frequency of the input time-varying frequency periodic signal. Then the model of time and space difference step size and signal to noise ratio (SNR) and relative frequency of quantum random filter is established by least square method. Finally, the parameters of the quantum filter can be determined step by step by analyzing the characteristics of the actual signal. The simulation results of single-frequency signal and frequency time-varying signal show that the proposed method can quickly and accurately design the optimal filter parameters based on the characteristics of the input signal, and achieve significant filtering effects.

摘要

设计最优时间和空间差分步长是量子随机滤波(QSF)实现时变频率周期信号滤波的关键技术。 本文提出用短时傅立叶变换(STFT)动态地估计输入时变频率周期信号的信噪比(SNR)和相对频率,用 最小二乘法建立量子随机滤波器时间和空间差分步长与信噪比(SNR)和相对频率的模型,通过分析实 际信号的特征可以逐步确定量子滤波器的参数。针对单频信号情况和频率时变信号的仿真实验结果表 明,该方法能够根据输入信号的特征,快速、准确设计出滤波器最优参数,取得显著的滤波效果。

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References

  1. [1]

    MÁRQUEZ F P G, NIETO M R M. Recurrent neural network and genetic algorithm approaches for a dual route optimization problem: A real case study [C]// Lecture Notes in Electrical Engineering. London: Springer, 2012: 23–37. DOI:https://doi.org/10.1007/978-1-4471-4600-12.

  2. [2]

    GANDHI V, ARORA V, BEHERA L, PRASAD G, COYLE D H, MCGINNITY T M. A recurrent quantum neural network model enhances the EEG signal for an improved brain-computer interface [C]// IET Seminar on Assisted Living. Piscataway, NJ, USA: IEEE, 2011: 12.

  3. [3]

    REBENTROST P, BROMLEY T R, WEEDBROOK C, LLOYD S. Quantum hopfield neural network [J]. Physical Review A, 2018, 98(4): 042308. DOI: https://doi.org/10.1103/PhysRevA.98.042308.

  4. [4]

    GANDHI V S, MCGINNITY T M. Quantum neural network based surface EMG signal filtering for control of robotic hand [C]// The 2013 International Joint Conference on Neural Networks (IJCNN). New York, USA: IEEE, 2013: 1–7.

  5. [5]

    GANDHI V, PRASAD G, COYLE D, BEHERA L, MCGINNITY T M. Quantum neural network-based EEG filtering for a brain-computer interface [J]. IEEE Transactions on Neural Networks and Learning Systems, 2014, 25(2): 278–288. DOI: https://doi.org/10.1109/TNNLS.2013.2274436.

  6. [6]

    FERNÁNDEZ E A, WILLSHAW P, PERAZZO C A, PRESEDO R J, BARRO S. Detection of abnormality in the electrocardiogram without prior knowledge by using the quantisation error of a self-organising map, tested on the European ischaemia database [J]. Medical & Biological Engineering & Computing, 2001, 39(3): 330–337. DOI: https://doi.org/10.1007/BF02345228.

  7. [7]

    LUITEL B, VENAYAGAMOORTHY G K. Quantum inspired PSO for the optimization of simultaneous recurrent neural networks as MIMO learning systems [J]. Neural Networks, 2010, 23(5): 583–586. DOI: https://doi.org/10.1016/j.neunet.2009.12.009.

  8. [8]

    HYOUNG-UK H, KIM J K. An evolutionary genetic neural networks for problems without prior knowledge [C]// 2014 10th International Conference on Natural Computation (ICNC). New York, USA: IEEE, 2014: 1–6.

  9. [9]

    KATZ G E, REGGIA J A. Using directional fibers to locate fixed points of recurrent neural networks [J]. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(8): 3636–3646. DOI: https://doi.org/10.1109/TNNLS.2017.2733544.

  10. [10]

    MU Yu-qiang, SHENG An-dong, GUO Zhi. Evolutionary diagonal recurrent neural network for nonlinear dynamic system identification [C]// 2008 IEEE International Conference on Networking, Sensing and Control. New York, USA: IEEE, 2008: 837–841.

  11. [11]

    KUMAGAI T, WADA M, HASHIMOTO R, UTSUGI A. Dynamical control by recurrent neural networks through genetic algorithms [J]. International Journal of Adaptive Control and Signal Processing, 2015, 13(4): 261–271. DOI: https://doi.org/10.1002/(SICI)1099-1115(199906)13:4<261::AID-ACS546>3.0.CO;2-N.

  12. [12]

    LI Zhan-ying, WANG Ke-jun, TANG Mo. Optimization of learning algorithms for chaotic diagonal recurrent neural networks [C]// 2010 International Conference on Intelligent Control and Information Processing. New York, USA: IEEE, 2010: 244–247.

  13. [13]

    CHEN Sheng-tan. Signal and systems [M]. Xi’an: Xi’an University of Electronic Science and Technology Press, 2001. (in Chinese)

  14. [14]

    GAO Xi-quan, DING Yu-mei. Digital signal processing [M]. Xi’an: Xi’an University of Electronic Science and Technology Press, 2016. (in Chinese)

  15. [15]

    WU Hao-han, JIN Fu-jiang, LAI Lian-you, WANG Liang. A stochastic filtering algorithm using Schrödinger equation [J]. Acta Automatica Sinica, 2014, 40(10): 2370–2376. DOI: https://doi.org/10.1016/S1874-1029(14)60366-9.

  16. [16]

    LAI Lian-you, JIN Fu-jiang, WU Hao-han. Quantum random filter denoising method for speech signal [J]. Information and Control, 2015, 44(5): 598–603. DOI: https://doi.org/10.13976/j.cnki.xk.2015.0598.(in Chinese)

  17. [17]

    CHEN Guang, REN Zhi-liang, SUN Hai-zhu. Least-squares curve fitting and Matlab realize [J]. Ordnance Industry Automation, 2005, 24(3): 107–108. (in Chinese)

  18. [18]

    LV Xi-ming, LI Ming-yuan. Least-squares curve-fitting in MATLAB [J]. Journal of Inner Mongolia University for Nationalities (Natural Sciences), 2009, 24(2): 125–127. DOI: https://doi.org/10.14045/j.cnki.15-1220.2009.02.035. (in Chinese)

  19. [19]

    KUANG Xiao-jing, WU Xian-liang, HUANG Zhi-xiang, WANG Dao-ping. Solving time-dependent Schrödinger formula based on FDTD method [C]// Proc National Conference on Microwave and Millimeter Wave. Xi’an, China: Publishing House of Electronics Industry, 2009: 990–993. (in Chinese)

  20. [20]

    GIORDANO N J, NAKANISHI H. Computational physics [M]. 2nd ed. Beijing, China: Tsinghua University Press, 2007.

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Acknowledgment

The authors would like to thank Prof. L BEHERA for guiding this paper. Besides, the authors would also like to thank the editors and anonymous reviewers for their time and effort spent handling this paper, as well as for providing constructive comments to further improve the presentation and quality of this paper.

Author information

Correspondence to Li-chun Zhou 周丽春.

Additional information

Foundation item: Projects(2017H0022, 2016H6015) supported by Fujian Science and Technology Key Project, China

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Cite this article

Zhou, L., Jin, F., Wu, H. et al. Parameters estimate of recurrent quantum stochastic filter for time variant frequency periodic signals. J. Cent. South Univ. 26, 3328–3337 (2019). https://doi.org/10.1007/s11771-019-4256-7

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Key words

  • quantum stochastic filter (QSF)
  • parameters estimation
  • least square (LS)
  • short-time Fourier transform (STFT)

关键词

  • 量子随机滤波器
  • 参数估计
  • 最小二乘
  • 短时傅立叶变换