The filtering based multi-innovation stochastic gradient algorithm for multi-input output-error systems

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• 英文论文题名：The filtering based multi-innovation stochastic gradient algorithm for multi-input output-error systems
• 论文作者：Jiling Ding
• 英文论文作者：Jiling Ding ; Department of Mathematics ; Jining University
• 年：2017
• 作者机构：Department of Mathematics, Jining University;
• 英文论文关键词：Multi-input output system ; Multi-innovation ; Stochastic gradient algorithm ; Filtering technique ; Parameter estimation
• 会议召开时间：2017-07-26
• 会议录名称：第36届中国控制会议论文集（A）
• 英文会议录名称：Proceedings of the 36th Chinese Control Conference（A）
• 语种：英文
• 分类号：N945.14
• 学会代码：KZLL
• 会议名称：第36届中国控制会议
• 会议地点：中国辽宁大连
• 主办单位：中国自动化学会控制理论专业委员会
• 学会名称：中国自动化学会控制理论专业委员会
• 页数：6
• 文件大小：184k
• 原文格式：D
• 会议级别：全国

摘要

In this paper, we discuss the identification problem of multi-input output-error moving average systems. A filtering based multi-innovation stochastic gradient algorithm with forgetting factors is derived by using the filtering technique with the multi-innovation identification theory, and a multi-innovation extended stochastic gradient algorithm is given for comparison.The simulation results confirm that the proposed algorithms can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm.
In this paper, we discuss the identification problem of multi-input output-error moving average systems. A filtering based multi-innovation stochastic gradient algorithm with forgetting factors is derived by using the filtering technique with the multi-innovation identification theory, and a multi-innovation extended stochastic gradient algorithm is given for comparison.The simulation results confirm that the proposed algorithms can generate highly accurate parameter estimates compared with the multi-innovation stochastic gradient algorithm.

引文

[1]F.M.Pinho,G.C.Franco,R.S.Silva,Modeling volatility using state space models with heavy tailed distributions,Mathematics and Computers in Simulation 119(2016)108-127.
    [2]M.M.da Silva,T.Wigren,T.Mendonc?a,Nonlinear identification of a minimal neuromuscular blockade model in anesthesia,IEEE Transactions on Control Systems Technology 20(1)(2012)181-188.
    [3]M.Hokr,D.Frydrych,Fully coupled algorithm for heat and water transport-Estimation of non-linear parameters based on the experimental data,Mathematics and Computers in Simulation 82(10)(2012)1908-1918.
    [4]Y.Shi,J.Huang,B.Yu,Robust tracking control of networked control systems:Application to a networked DC motor,IEEE Transactions on Industrial Electronics 60(12)(2013)5864-5874.
    [5]Y.Ji,X.M.Liu,Unified synchronization criteria for hybrid switching-impulsive dynamical networks,Circuits,Systems and Signal Processing 34(5)(2015)1499-1517.
    [6]T.Bonin,H.Fa?bender,A.Soppa,M.Zaeh,A fully adaptive rational global Arnoldi method for the model-order reduction of second-order MIMO systems with proportional damping,Mathematics and Computers in Simulation 122(2016)1-19.
    [7]Y.Shi,B.Yu,Robust mixed H-2/H-infinity control of networked control systems with random time delays in both forward and backward communication links,Automatica 47(4)(2011)754-760.
    [8]P.D.Alevizos,An optimal algorithm for computing the nontrivial circuits of a union of iso-oriented rectangles,Information Processing Letters 113(3)(2013)55-59.
    [9]R.Venkata Rao,V.D.Kalyankar,G.Waghmare,Parameters optimization of selected casting processes using teachinglearning-based optimization algorithm,Applied Mathematical Modelling 38(2014)5592-5608.
    [10]G.M.Zhuang,Y.M.Li,Z.Li,Fault detection for a class of uncertain nonlinear Markovian jump stochastic systems with mode-dependent time delays and sensor saturation,International Journal of Systems Science 47(7)(2016)1514-1532.
    [11]B.T.Thumati,S.Jagannathan,A model-based fault-detection and prediction scheme for nonlinear multivariable discretetime systems with asymptotic stability guarantees,IEEE Transactions on Neural Networks 21(3)(2010)404-423.
    [12]G.Merc e`re,L.Bako,Parameterization and identification of multivariable state-space systems:a canonical approach,Automatica 47(8)(2011)1547-1555.
    [13]S.Jian,Z.A.Ma,T.N.Li,D.N.Niu,Development and application of an integrated traffic simulation and multi-driving simulators,Simulation Modelling Practice and Theory(59)(2015)1-17.
    [14]A.Nasirin,S.K.Nguang,A.Swain,Adaptive sliding mode control for a class of MIMO nonlinear systems with uncertainties,Journal of the Franklin Institute 351(2014)2048-2061.
    [15]G.Tao,Multivariable adaptive control:A survey,Automatica50(12)(2014)2737-2764
    [16]Y.Zhang,Unbiased identification of a class of multi-input single-output systems with correlated disturbances using bias compensation methods,Mathematical and Computer Modelling 53(9-10)(2011)1810-1819.
    [17]S.Mobayen,An LMI-based robust tracker for uncertain linear systems with multiple time-varying delays using optimal composite nonlinear feedback technique,Nonlinear Dynamics80(2015)917-927.
    [18]J.X.Ma,F.Ding,Recursive relations of the cost functions for the least squares algorithms for multivariable systems,Circuits,Systems and Signal Processing 32(1)(2013)83-101.
    [19]W.X.Zheng,Least-squares identification of a class of multivariable systems with correlated disturbances,Journal of the Franklin Institute–Engineering and Applied Mathematics336(8)(1999)1309-1324.
    [20]D.Q.Wang,W.Zhang,Improved least squares identification algorithm for multivariable Hammerstein systems,Journal of the Franklin Institute 352(2015)5292-5307.
    [21]Y.Boularouk,K.Djeddour,New approximation for ARMA parameters estimate,Mathematics and Computers in Simulation 118(2015)116-122.
    [22]Y.B.Hu,Iterative and recursive least squares estimation algorithms for moving average systems,Simulation Modelling Practice and Theory 34(2013)12-19.
    [23]M.Jafari,M.Salimifard,M.Dehghani,Identification of multivariable nonlinear systems in the presence of colored noises using iterative hierarchical least squares algorithm,ISA Transactions 53(4)(2014)1243-1252.
    [24]F.Ikhouane,F.Giri,A unified approach for the parametric identification of SISO/MIMO Wiener and Hammerstein systems,Journal of the Franklin Institute 351(3)(2014)1717-1727.
    [25]M.S.H o¨lzel,D.S.Bernstein,A matrix nullspace approach for solving equality-constrained multivariable polynomial leastsquares problems,Automatica 50(12)(2014)3030-3037.
    [26]C.Wang,L.Zhu,Parameter identification of a class of nonlinear systems based on the multi-innovation identification theory,Journal of the Franklin Institute 352(10)(2015)4624-4637.
    [27]Y.B.Hu,B.L.Liu,Q.Zhou,A multi-innovation generalized extended stochastic gradient algorithm for output nonlinear autoregressive moving average systems,Applied Mathematics and Computation 247(2014)218-224.
    [28]X.H.Wang,F.Ding,Convergence of the auxiliary model based multi-innovation generalized extended stochastic gradient algorithm for Box-Jenkins systems,Nonlinear Dynamics82(1-2)(2015)269-280.
    [29]Q.B.Jin,Z.Wang,X.P.Liu,Auxiliary model-based intervalvarying multi-innovation least squares identification for multivariable OE-like systems with scarce measurements,Journal of Process Control 35(11)(2015)154-168.
    [30]J.Ma,S.L.Sun,Optimal linear estimators for multi-sensor stochastic uncertain systemswith packet losses of both sides,Digital Signal Processing 37(2015)24-34.
    [31]K.Mathiyalagan,J.H.Park,R.Sakthivel,Robust reliable dissipative filtering for networked control systems with sensor failure,IET Signal Process 8(2014)809-822.
    [32]F.Ding,X.M.Liu,X.Y.Ma,Kalman state filtering based least squares iterative parameter estimation for observer canonical state space systems using decomposition,Journal of Computational and Applied Mathematics 301(2016)135-143.
    [33]Y.W.Mao,F.Ding,A novel data filtering based multiinnovation stochastic gradient algorithm for Hammerstein nonlinear systems,Digital Signal Processing 46(2015)215-225.