Search

Article

x

留言板

尊敬的读者、作者、审稿人, 关于本刊的投稿、审稿、编辑和出版的任何问题, 您可以本页添加留言。我们将尽快给您答复。谢谢您的支持!

姓名
邮箱
手机号码
标题
留言内容
验证码

Intelligent particle filter based on bat algorithm

Chen Zhi-Min Tian Meng-Chu Wu Pan-Long Bo Yu-Ming Gu Fu-Fei Yue Cong

Citation:

Intelligent particle filter based on bat algorithm

Chen Zhi-Min, Tian Meng-Chu, Wu Pan-Long, Bo Yu-Ming, Gu Fu-Fei, Yue Cong
PDF
Get Citation

(PLEASE TRANSLATE TO ENGLISH

BY GOOGLE TRANSLATE IF NEEDED.)

  • Particle filer is apt to have particle impoverishment with unstable filtering precision, and a large number of granules are required to estimate the nonlinear system accurately, which reduces the comprehensive performance of the algorithm. To solve this problem, a new particle filter based on bat algorithm is presented in this paper, where particles are used to represent individual bat so as to imitate the search process of bats for preys. In traditional resampling process, particles are directly discarded, the improved algorithm adopts another approach and solves the problem of particle impoverishment. It combines the advantages of particle swarm optimization algorithm and harmonic algorithm perfectly. New particle filter has capacity of global and local search and is superior in computation accuracy and efficiency. By adjusting frequency, loudness, and impulse emissivity of particle swarm, the optimal particle at that time is followed by particle swarm to search in the solution space. The global search and local search can be switched dynamically to improve the overall quality of the particles swarm as well as the distribution rationality. In addition, the improved particle filter uses Lvy flight strategy to avoid being attracted by harmful local optimal solution, it expands the space of research and further promotes the optimization effect of particle distribution. Using the useful information about particle swarm, improved particle filter can make particles get rid of local optimum and reduce the waste of iterations in insignificant status change. Based on the number of valid particle samples, it can improve quality of particle samples by expanding their diversity. In information interaction mechanism of improved particle filter, the method in this paper sets scoreboard of particle target function to compare the value of particle target function at each iteration sub-moment with the value of target function on scoreboard to gain global optimum of all particles at current filtering moment. Taking information interaction between global optimum and particle swarm, the guiding function of global optimum is realized. The process of particle optimization is ended prematurely through setting a maximum iteration or termination threshold. There is a tendency for the whole particle swarm closing to high likehood area without global convergence so that the advantages of improved particle filter in accuracy and speed will not be damaged. In addition, convergence analysis and computational complexity analysis are given in this paper. Experiment indicates that this method can improve the particle diversity and prediction accuracy of particle filter, and meanwhile reduce the particle quantity obviously which is required by the state value prediction for nonlinear system.
      Corresponding author: Chen Zhi-Min, chenzhimin@188.com
    • Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 61501521, U1330133, 61473153) and the China Postdoctoral Science Foundation (Grant No. 2015M582861).
    [1]

    Hossein T N, Akihiro T, Seiichi M 2012IEEE Trans.Intell.Transp.Syst. 13 748

    [2]

    Li H W, Wang J 2012IET Radar Sonar Navig. 6 180

    [3]

    Vasileios M, Panos S 2012J.Comput.Phys. 231 602

    [4]

    Yang W M, Zhao M R 2016Acta Phys.Sin. 65 040502(in Chinese)[杨伟明, 赵美蓉2016物理学报65 040502]

    [5]

    Du M, Nan X M, Guan L 2013IEEE Trans.Image Process. 22 3852

    [6]

    Chen Z M, Qu Y X, Liu B, Fu M H, Chen J H 2016Proc.Inst.Mech.Eng.G:J.Aerosp.Engineering 230 747

    [7]

    Zhang Q, Qiao Y K, Kong X Y, Si X S 2014Acta Phys.Sin. 63 110505(in Chinese)[张琪, 乔玉坤, 孔祥玉, 司小胜2014物理学报63 110505]

    [8]

    Wang X, Han C Z 2013Acta Automatica Sinica 39 1152(in Chinese)[王晓, 韩崇昭2013自动化学报39 1152]

    [9]

    Zhang Q, Hu C H, Qiao Y K 2008Control and Decision 23 117(in Chinese)[张琪, 胡昌华, 乔玉坤2008控制与决策23 117]

    [10]

    Li T, Sattar T P, Sun S 2012Signal Process. 92 1637

    [11]

    Pawel M S, Zsfia L, Robert B 2013Automatica 49 147

    [12]

    Yu Y, Zheng X 2011Signal Process. 91 1339

    [13]

    Zhong J, Fung Y F 2012IET Control Theory Appl. 6 689

    [14]

    Xian W, Long B, Li M, Wang H 2013IEEE Trans.Instrum.Meas. 63 2

    [15]

    Liu Y L, Lin B J 2010Control and Decision 25 361(in Chinese)[刘云龙, 林宝军2010控制与决策25 361]

    [16]

    Qiu X N, Liu S R, LQ 2010Control TheoryApplications 27 1724(in Chinese)[邱雪娜, 刘士荣, 吕强2010控制理论与应用27 1724]

    [17]

    Chen Z M, Bo Y M, Wu P L, Duan W Y, Liu Z F 2013Control and Decision 28 193(in Chinese)[陈志敏, 薄煜明, 吴盘龙, 段文勇, 刘正凡2013控制与决策28 193]

    [18]

    Gandomi A H, Yang X S, Alavi A H, Talatahari S 2013Neural Comput.Appl. 22 1239

    [19]

    Li L L, Zhou Y Q 2014Neural Comput.Appl. 25 1369

    [20]

    Yao Z N, Liu D M, Liu S D, Zhu X L 2014Acta Phys.Sin. 63 227502(in Chinese)[姚振宁, 刘大明, 刘胜道, 朱兴乐2014物理学报63 227502]

    [21]

    Rodrigues D, Pereira L A M, Nakamura R Y M, Costa K A P, Yang X S, Souza A N 2014Expert Syst.Appl. 41 2250

    [22]

    Chen Z M, Qu Y X, Xi Z D, Liu B, Kang D Y 2016Asian J.Control 18 1877

  • [1]

    Hossein T N, Akihiro T, Seiichi M 2012IEEE Trans.Intell.Transp.Syst. 13 748

    [2]

    Li H W, Wang J 2012IET Radar Sonar Navig. 6 180

    [3]

    Vasileios M, Panos S 2012J.Comput.Phys. 231 602

    [4]

    Yang W M, Zhao M R 2016Acta Phys.Sin. 65 040502(in Chinese)[杨伟明, 赵美蓉2016物理学报65 040502]

    [5]

    Du M, Nan X M, Guan L 2013IEEE Trans.Image Process. 22 3852

    [6]

    Chen Z M, Qu Y X, Liu B, Fu M H, Chen J H 2016Proc.Inst.Mech.Eng.G:J.Aerosp.Engineering 230 747

    [7]

    Zhang Q, Qiao Y K, Kong X Y, Si X S 2014Acta Phys.Sin. 63 110505(in Chinese)[张琪, 乔玉坤, 孔祥玉, 司小胜2014物理学报63 110505]

    [8]

    Wang X, Han C Z 2013Acta Automatica Sinica 39 1152(in Chinese)[王晓, 韩崇昭2013自动化学报39 1152]

    [9]

    Zhang Q, Hu C H, Qiao Y K 2008Control and Decision 23 117(in Chinese)[张琪, 胡昌华, 乔玉坤2008控制与决策23 117]

    [10]

    Li T, Sattar T P, Sun S 2012Signal Process. 92 1637

    [11]

    Pawel M S, Zsfia L, Robert B 2013Automatica 49 147

    [12]

    Yu Y, Zheng X 2011Signal Process. 91 1339

    [13]

    Zhong J, Fung Y F 2012IET Control Theory Appl. 6 689

    [14]

    Xian W, Long B, Li M, Wang H 2013IEEE Trans.Instrum.Meas. 63 2

    [15]

    Liu Y L, Lin B J 2010Control and Decision 25 361(in Chinese)[刘云龙, 林宝军2010控制与决策25 361]

    [16]

    Qiu X N, Liu S R, LQ 2010Control TheoryApplications 27 1724(in Chinese)[邱雪娜, 刘士荣, 吕强2010控制理论与应用27 1724]

    [17]

    Chen Z M, Bo Y M, Wu P L, Duan W Y, Liu Z F 2013Control and Decision 28 193(in Chinese)[陈志敏, 薄煜明, 吴盘龙, 段文勇, 刘正凡2013控制与决策28 193]

    [18]

    Gandomi A H, Yang X S, Alavi A H, Talatahari S 2013Neural Comput.Appl. 22 1239

    [19]

    Li L L, Zhou Y Q 2014Neural Comput.Appl. 25 1369

    [20]

    Yao Z N, Liu D M, Liu S D, Zhu X L 2014Acta Phys.Sin. 63 227502(in Chinese)[姚振宁, 刘大明, 刘胜道, 朱兴乐2014物理学报63 227502]

    [21]

    Rodrigues D, Pereira L A M, Nakamura R Y M, Costa K A P, Yang X S, Souza A N 2014Expert Syst.Appl. 41 2250

    [22]

    Chen Z M, Qu Y X, Xi Z D, Liu B, Kang D Y 2016Asian J.Control 18 1877

  • [1] Zhuang Jie, Han Rui, Ji Zhen-Yu, Shi Fu-Kun. Uncertainty in prediction of pulsed field ablation caused by parameter diversity in quantifying conductivity models. Acta Physica Sinica, 2023, 72(14): 147701. doi: 10.7498/aps.72.20230203
    [2] Guo Li-Ren, Hu Yi-Hua, Dong Xiao, Li Min-Le. Translation compensation and micro-motion parameter estimation of laser micro-Doppler effect. Acta Physica Sinica, 2018, 67(15): 150701. doi: 10.7498/aps.67.20172754
    [3] Yang Wei-Ming, Zhao Mei-Rong. Auto-adjust lag particle filter smoothing algorithm for non-linear state estimation. Acta Physica Sinica, 2016, 65(4): 040502. doi: 10.7498/aps.65.040502
    [4] Wu Hao, Chen Shu-Xin, Yang Bin-Feng, Chen Kun. Robust cubature Kalman filter target tracking algorithm based on genernalized M-estiamtion. Acta Physica Sinica, 2015, 64(21): 218401. doi: 10.7498/aps.64.218401
    [5] Huang Yu, Liu Yu-Feng, Peng Zhi-Min, Ding Yan-Jun. Research on particle swarm optimization algorithm with characteristic of quantum parallel and its application in parameter estimation for fractional-order chaotic systems. Acta Physica Sinica, 2015, 64(3): 030505. doi: 10.7498/aps.64.030505
    [6] Yao Zhen-Ning, Liu Da-Ming, Liu Sheng-Dao, Zhu Xing-Le. A real-time magnetic localization method of underwater non-cooperative magnetic targets based on unscented particle filter. Acta Physica Sinica, 2014, 63(22): 227502. doi: 10.7498/aps.63.227502
    [7] Chen Ying, Wang Wen-Yue, Yu Na. Improvement of the filtering performance of a heterostructure photonic crystal ring resonator using PSO algorithm. Acta Physica Sinica, 2014, 63(3): 034205. doi: 10.7498/aps.63.034205
    [8] Zhang Shu-Ning, Zhao Hui-Chang, Xiong Gang, Guo Chang-Yong. Separation and parameter estimation of single channel sinusoidal frequency modulated signal mixture sources based on particle filtering. Acta Physica Sinica, 2014, 63(15): 158401. doi: 10.7498/aps.63.158401
    [9] Zhang Qi, Qiao Yu-Kun, Kong Xiang-Yu, Si Xiao-Sheng. Study on stochastic perturbation strong tracking particle filter. Acta Physica Sinica, 2014, 63(11): 110505. doi: 10.7498/aps.63.110505
    [10] Liu Tun-Dong, Chen Jun-Ren, Hong Wu-Peng, Shao Gui-Fang, Wang Ting-Na, Zheng Ji-Wen, Wen Yu-Hua. Particle swarm optimization investigation of stable structures of Pt-Pd alloy nanoparticles. Acta Physica Sinica, 2013, 62(19): 193601. doi: 10.7498/aps.62.193601
    [11] Zhang Hong-Li, Song Li-Li. Parameter identification in chaotic systems by means of quantum particle swarm optimization. Acta Physica Sinica, 2013, 62(19): 190508. doi: 10.7498/aps.62.190508
    [12] Li Pan-Chi, Wang Hai-Ying, Song Kao-Ping, Yang Er-Long. Research on the improvement of quantum potential well-based particle swarm optimization algorithm. Acta Physica Sinica, 2012, 61(6): 060302. doi: 10.7498/aps.61.060302
    [13] Sheng Zheng, Chen Jia-Qing, Xu Ru-Hai. Tracking refractivity from radar clutter using particle filter. Acta Physica Sinica, 2012, 61(6): 069301. doi: 10.7498/aps.61.069301
    [14] Leng Hong-Ze, Song Jun-Qiang, Cao Xiao-Qun, Yang Jin-Hui. Improved particle filter in data assimilation. Acta Physica Sinica, 2012, 61(7): 070501. doi: 10.7498/aps.61.070501
    [15] Fang Wei, Sun Jun, Xie Zhen-Ping, Xu Wen-Bo. Convergence analysis of quantum-behaved particle swarm optimization algorithm and study on its control parameter. Acta Physica Sinica, 2010, 59(6): 3686-3694. doi: 10.7498/aps.59.3686
    [16] Zhu Zhi-Yu, Yang Guan-Xiao. Stiefel manifold particle filtering. Acta Physica Sinica, 2010, 59(12): 8316-8321. doi: 10.7498/aps.59.8316
    [17] Ning Xiao-Lei, Wang Hong-Li, Zhang Qi, Chen Lian-Hua. Interval diffracted particle filter. Acta Physica Sinica, 2010, 59(7): 4426-4433. doi: 10.7498/aps.59.4426
    [18] Gao Fei, Tong Heng-Qing. Parameter estimation for chaotic system based on particle swarm optimization. Acta Physica Sinica, 2006, 55(2): 577-582. doi: 10.7498/aps.55.577
    [19] Du Zheng-Cong, Tang Bin, Li Ke. The hybrid annealed particle filter. Acta Physica Sinica, 2006, 55(3): 999-1004. doi: 10.7498/aps.55.999
    [20] LI YAN, CHEN JIAN-GUO, LI DA-YI, LU YANG, ZHOU XIAO-HONG. VARIOUS HYSTERESIS LOOPS AND MULTISTABILITY ON OUTPUT CURVES OF TUNABLE EXTERNAL CAVITY SEMICONDUCTOR LASERS. Acta Physica Sinica, 1999, 48(12): 2252-2258. doi: 10.7498/aps.48.2252
Metrics
  • Abstract views:  5603
  • PDF Downloads:  390
  • Cited By: 0
Publishing process
  • Received Date:  24 August 2016
  • Accepted Date:  11 December 2016
  • Published Online:  05 March 2017

/

返回文章
返回