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基于多尺度径向基函数的时变系统辨识

刘青 李阳

刘青, 李阳. 基于多尺度径向基函数的时变系统辨识[J]. 北京航空航天大学学报, 2015, 41(9): 1722-1728. doi: 10.13700/j.bh.1001-5965.2014.0693
引用本文: 刘青, 李阳. 基于多尺度径向基函数的时变系统辨识[J]. 北京航空航天大学学报, 2015, 41(9): 1722-1728. doi: 10.13700/j.bh.1001-5965.2014.0693
LIU Qing, LI Yang. Identification of time-varying systems using multi-scale radial basis function[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(9): 1722-1728. doi: 10.13700/j.bh.1001-5965.2014.0693(in Chinese)
Citation: LIU Qing, LI Yang. Identification of time-varying systems using multi-scale radial basis function[J]. Journal of Beijing University of Aeronautics and Astronautics, 2015, 41(9): 1722-1728. doi: 10.13700/j.bh.1001-5965.2014.0693(in Chinese)

基于多尺度径向基函数的时变系统辨识

doi: 10.13700/j.bh.1001-5965.2014.0693
基金项目: 国家自然科学基金(61403016); 高等学校博士学科点专项科研基金(20131102120008); 教育部留学回国人员科研启动基金 (60300002014103001); 中央高校基本科研业务费专项资金(YWF-14-ZDHXY-020)
详细信息
    作者简介:

    刘青(1991—),女,河北沧州人,硕士研究生,lqyueming_2009@163.com

    通讯作者:

    李阳(1980—),男,湖南邵阳人,副教授,liyang@buaa.edu.cn,主要研究方向为复杂系统建模、信号处理与机器学习.

  • 中图分类号: N945.14

Identification of time-varying systems using multi-scale radial basis function

  • 摘要: 应用非平稳时间序列的时变系统建模方法进行了参数随时间变化的线性系统参数的辨识.通过引入多尺度径向基函数(MRBF)将非平稳过程的辨识问题转化为线性时不变过程的辨识,结合粒子群优化算法(PSO)获得时变系统参数估计的最优径向基函数(RBF)尺度.由于RBF具有良好的局部特性且尺度可以调整,采用RBF作为基函数可以更好地识别具有多种动态过程的时变系统参数.通过对时变系数包含多种波形的二阶时变自回归模型进行仿真辨识,与采用传统的递推最小二乘法和勒让德多项式作为基函数展开式方法相比,提出的方法对于时变系统参数具有更好的跟踪能力,验证了辨识方法的有效性.

     

  • [1] Boashash B,Azemi G,Toole J M.Time-frequency processing of nonstationary signals:Advanced TFD design to aid diagnosis with highlights from medical applications[J].IEEE Signal Processing Magazine,2013,30(6):108-119.
    [2] 续秀忠,张志谊,华宏星,等.应用时变系数建模方法辨识时变模态参数[J].航空学报,2003,24(3):230-233.Xu X Z,Zhang Z Y,Hua H X,et al.Identification of time-variant modal parameters by a time-varying parametric approach[J].Acta Aeronautica et Astronautica Sinica,2003,24(3):230-233(in Chinese).
    [3] Niedzwiecki M.Identification of time-varying process[M].New York:John Wiley & Sons,2000:83-94.
    [4] 顾海雷,史治宇,许鑫.基于B样条小波基函数时变多变量AR模型的时变结构参数识别[J].振动与冲击,2013,32(19):86-92.Gu H L,Shi Z Y,Xu X.Parameter identification for a time-varying structure using a multi-variable AR model based on interval B-spline wavelet base functions[J].Journal of Vibration and Shock,2013,32(19):86-92(in Chinese).
    [5] 于开平,庞世伟,赵婕.时变线性/非线性结构参数识别及系统辨识方法研究进展[J].科学通报,2009,54(20):3147-3156.Yu K P,Pang S W,Zhao J.Advances in method of time-varying linear/nonlinear structural system identification and parameter estimate[J].Chinese Science Bulletin,2009,54(20):3147-3156(in Chinese).
    [6] 陈宇,陈怀海,李赞澄,等.基于时变AR模型和小波变换的时变系数识别[J].国外电子测量技术,2011,30(7):20-23.Chen Y,Chen H H,Li Z C,et al.Identification of time-varying parameters base on time-varying AR model and wavelet transform[J].Foreign Electronic Measurement Technology,2011,30(7):20-23(in Chinese).
    [7] Li Y,Wei H L,Billings S A,et al.Time-varying model identification for time-frequency feature extraction from EEG data[J].Journal of Neuroscience Methods,2011,196:151-158.
    [8] Chon K H,Zhao H,Zou R,et al.Multiple time-varying dynamics analysis usingmultiple sets of basis functions[J].IEEE Transactions on Biomedical Engineering,2005,52(5):956-960.
    [9] Chui C K,Wang J H.On compactly supported spline wavelets and a duality principle[J].Transactions of American Mathematical Society,1992,330:903-915.
    [10] Li Y,Wei H L,Billings S A.Identification of time-varying systems using multi-wavelet basis functions[J].IEEE Transactions on Control Systems Technology,2011,19(3):656-663.
    [11] Billings S A,Wei H L,Balikhin M A.Generalized multiscale radial basis function networks[J].Neural Networks,2007,20(10):1081-1094.
    [12] 李秀英,韩志刚.一种基于粒子群优化的非线性系统辨识方法[J].控制与决策,2011,26(11):1627-1631.Li X Y,Han Z G.Identification approach for nonlinear systems based on particle swarm optimization[J].Control and Decision,2011,26(11):1627-1631(in Chinese).
    [13] 秦玉灵,孔宪仁,罗文波.基于径向基函数响应面的机翼有限元模型修正[J].北京航空航天大学学报,2011,37(11):1465-1470.Qin Y L,Kong X R,Luo W B.Finite element model updating of airplane wing based on Gaussian radial basis function response surface[J].Journal of Beijing University of Aeronautics and Astronautics,2011,37(11):1465-1470(in Chinese).
    [14] Chang L C,Chang F J,Wang Y P.Auto-configuring radial basis function networks for chaotic time series and flood forecasting[J].Hydrological Process,2009,23(17):2450-2459.
    [15] 孙健,吴森堂.基于改进粒子群优化算法的巡航导弹航路规划[J].北京航空航天大学学报,2011,37(10):1228-1232.Sun J,Wu S T.Route planning of cruise missile based on improved particle swarm algorithm[J].Journal of Beijing University of Aeronautics and Astronautics,2011,37(10):1228-1232(in Chinese).
    [16] Chen S,Hong X,Bing L L,et al.Non-linear system identification using particle swarm optimization tuned radial basis function models[J].International Journal of Bio-Inspired Computation,2009,1(4):246-258.
    [17] Pinkey C,Kusum D,Millie P.Novel inertia weight strategies for particle swarm optimization[J].Memetic Computing,2013,5(3):229-251.
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出版历程
  • 收稿日期:  2014-11-11
  • 刊出日期:  2015-09-20

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