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多元伪线性回归系统部分耦合 多新息随机梯度类辨识方法
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国家自然科学基金（61273194）；江苏省自然科学基金（BK2012549）


Partially coupled multi-innovation stochastic gradient type identification methods for multivariate pseudo-linear regressive systems
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    摘要:

    针对多元伪线性滑动平均系统，讨论了多元增广随机梯度算法，为减小算法的计算量，将系统分解为一些子系统，给出了子系统增广随机梯度算法，利用耦合辨识概念和多新息辨识理论，推导了部分耦合（子系统）增广随机梯度算法、部分耦合（子系统）多新息增广随机梯度算法.进一步将提出的方法推广到多元伪线性自回归滑动平均系统，给出了部分耦合（子系统）广义增广随机梯度算法、部分耦合（子系统）多新息广义增广随机梯度算法.文中分析了多元增广随机梯度算法、部分耦合增广随机梯度算法、部分耦合多新息增广随机梯度算法的计算量.

    Abstract:

    For multivariate pseudo-linear regressive moving average systems,a multivariate extended stochastic gradient(ESG) algorithm is discussed.In order to reduce the computational cost of the identification algorithm,we decompose a multivariate system into several subsystems,and derive a partially coupled(subsystem) ESG algorithm and a partially coupled(subsystem) multi-innovation ESG algorithm according to the coupling identification concept and the multi-innovation identification theory.Furthermore,we extend these methods to multivariate pseudo-linear autoregressive moving average systems and present a partially coupled(subsystem) generalized extended stochastic gradient(GESG) algorithm and a partially coupled(subsystem) multi-innovation GESG algorithm.The computational efficiencies of the multivariate ESG algorithm,the partially coupled ESG algorithm and the partially coupled multi-innovation ESG algorithm are analyzed.

    参考文献
    [1] 丁锋.系统辨识新论[M].北京:科学出版社,2013 DING Feng.System identification:New theory and methods[M].Beijing:Science Press,2013
    [2] 丁锋.系统辨识:辨识方法性能分析[M].北京:科学出版社,2014 DING Feng.System identification:Performance analysis for identification methods[M].Beijing:Science Press,2014
    [3] 丁锋.系统辨识(1):辨识导引[J].南京信息工程大学学报:自然科学版,2011,3(1):1-22 DING Feng.System identification.Part A:Introduction to the identification[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2011,3(1):1-22
    [4] 丁锋.系统辨识(2):系统描述的基本模型[J].南京信息工程大学学报:自然科学版,2011,3(2):97-117 DING Feng.System identification.Part B:Basic models for system description[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2011,3(2):97-117
    [5] 丁锋.系统辨识(3):辨识精度与辨识基本问题[J].南京信息工程大学学报:自然科学版,2011,3(3):193-226 DING Feng.System identification.Part C:Identification accuracy and basic problems[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2011,3(3):193-226
    [6] 丁锋.系统辨识(4):辅助模型辨识思想与方法[J].南京信息工程大学学报:自然科学版,2011,3(4):289-318 DING Feng.System identification.Part D:Auxiliary model identification idea and methods[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2011,3(4):289-318
    [7] 丁锋.系统辨识(5):迭代搜索原理与辨识方法[J].南京信息工程大学学报:自然科学版,2011,3(6):481-510 DING Feng.System identification.Part E:Iterative search principle and identification methods[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2011,3(6):481-510
    [8] 丁锋.系统辨识(6):多新息辨识理论与方法[J].南京信息工程大学学报:自然科学版,2012,4(1):1-28 DING Feng.System identification.Part F:Multi-innovation identification theory and methods[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2012,4(1):1-28
    [9] 丁锋.系统辨识(7):递阶辨识原理与方法[J].南京信息工程大学学报:自然科学版,2012,4(2):97-124 DING Feng.System identification.Part G:Hierarchical identification principle and methods[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2012,4(2):97-124
    [10] 丁锋.系统辨识(8):耦合辨识概念与方法[J].南京信息工程大学学报:自然科学版,2012,4(3):193-212 DING Feng.System identification.Part H:Coupling identification concept and methods[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2012,4(3):193-212
    [11] 丁锋.辨识方法的计算效率(1):递推算法[J].南京信息工程大学学报:自然科学版,2012,4(4):289-300 DING Feng.Computational efficiency of the identification methods.Part A:Recursive algorithms[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2012,4(4):289-300
    [12] 丁锋.辨识方法的计算效率(2):迭代算法[J].南京信息工程大学学报:自然科学版,2012,4(5):385-401 DING Feng.Computational efficiency of the identification methods.Part B:Iterative algorithms[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2012,4(5):385-401
    [13] 丁锋.辨识方法的计算效率(3):信息向量耦合算法[J].南京信息工程大学学报:自然科学版,2012,4(6):481-495 DING Feng.Computational efficiency of the identification methods.Part C:Coupled information vector algorithms[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2012,4(6):481-495.
    [14] 丁锋,汪菲菲.多元系统耦合多新息随机梯度类辨识方法[J].南京信息工程大学学报:自然科学版,2014,6(1):1-16 DING Feng,WANG Feifei.Coupled multi-innovation stochastic gradient type identification methods for multivariate systems[J].Journal of Nanjing University of Information Science and Technology:Natural Science Edition,2014,6(1):1-16
    [15] Ding F,Liu X P,Liu G.Multi-innovation least squares identification for system modeling[J].IEEE Transactions on Systems,Man,and Cybernetics,Part B:Cybernetics,2010,40(3):767-778
    [16] 丁锋,杨家本.大系统的递阶辨识[J].自动化学报,1999,25(5):647-654 DING Feng,YANG Jiaben.Hierarchical identification of large scale systems[J].Acta Automatica Sinica,1999,25(5):647-654
    [17] Ding F,Liu G,Liu X P.Partially coupled stochastic gradient identification methods for non-uniformly sampled systems[J].IEEE Transactions on Automatic Control,2010,55(8):1976-1981
    [18] Ding F.Coupled-least-squares identification for multivariable systems[J].IET Control Theory and Applications,2013,7(1):68-79
    [19] Ding F,Chen T.Hierarchical gradient-based identification of multivariable discrete-time systems[J].Automatica,2005,41(2):315-325
    [20] 丁锋,丁韬,杨家本,等.时变参数遗忘梯度估计算法的收敛性[J].自动化学报,2002,28(6):962-968 DING Feng,DING Tao,YANG Jiaben,et al.Convergence of forgetting gradient estimation algorithm for time-varying parameters[J].Acta Automatica Sinica,2002,28(6):962-968
    [21] Ding F,Chen T.Performance analysis of multi-innovation gradient type identification methods[J].Automatica,2007,43(1):1-14
    [22] Ding J,Shi Y,Wang H G,et al.A modified stochastic gradient based parameter estimation algorithm for dual-rate sampled-data systems[J].Digital Signal Processing,2010,20(4):1238-1247
    [23] Chen J,Ding F.Modified stochastic gradient algorithms with fast convergence rates[J].Journal of Vibration and Control,2011,17(9):1281-1286
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丁锋,汪菲菲,汪学海.多元伪线性回归系统部分耦合 多新息随机梯度类辨识方法[J].南京信息工程大学学报(自然科学版),2014,6(2):97-112
DING Feng, WANG Feifei, WANG Xuehai. Partially coupled multi-innovation stochastic gradient type identification methods for multivariate pseudo-linear regressive systems[J]. Journal of Nanjing University of Information Science & Technology, 2014,6(2):97-112

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历史
  • 收稿日期:2014-04-08
  • 在线发布日期: 2014-04-21

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