共查询到17条相似文献,搜索用时 421 毫秒
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时变系统有限数据窗最小二乘辨识的有界收敛性 总被引:8,自引:0,他引:8
利用随机过程理论证明了有限数据窗最小二乘法的有界收敛性,给出了参数估计误差
上界的计算公式,阐述了获得最小均方参数估计误差上界时数据窗长度的选择方法.分析表明,
对于时不变随机系统,数据窗长度越大,均方参数估计误差上界越小;对于确定性时变系统,数
据窗长度越小,均方参数估计误差上界越小.因此,对于时变随机系统,一个折中方案是寻求一
个最佳数据窗长度,以使均方参数估计误差最小.该文的研究成果对于提高辨识算法的实际应
用效果有重要意义. 相似文献
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时变系统最小均方算法的性能分析 总被引:4,自引:1,他引:3
在无过程数据平稳性假设和各态遍历等条件下,运用随机过程理论研究了最小方算法(LMS)的有界收敛性,给出了估计误差的上界,论述了LMS算法收敛因子或步长的选择方法,以使参数估计误差上界最小。这对于提高LMS算法的实际应用效果有着重要意义。LMS算法的收敛性分析表明:(1)对于确定性时不变系统,LMS算法是指数速度收敛的;(2)对于确定性时变系统,收敛因子等于1,LMS算法的参数估计误差上界最小;(3)对于时变或不变随机系统,LMS算法的参数估计误差一致有上界。 相似文献
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基于辅助模型的量化控制系统辨识方法 总被引:1,自引:1,他引:0
针对具有通信约束的量化控制系统模型, 在采用随机重复性试验测量信息的技术上, 提出了基于辅助模型的量化系统参数辨识方法. 首先分析了在随机重复性试验方法下量化系统的模型特征并给出了分两步辨识的策略.分析表明, 在上述模型里系统具有时变的估计误差, 推导了进行参数辨识所满足的持续激励条件, 并给出了基于辅助模型的多新息量化辨识递推算法. 接着研究了所给出辨识算法的收敛性分析, 得到了系统参数估计误差上界的计算式,最后将方法推广到一类Hammerstein非线性系统量化辨识问题上. 数字仿真验证了该算法及结论 相似文献
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关于鞅超收敛定理与遗忘因子最小二乘算法的收敛性分析 总被引:13,自引:3,他引:10
鞅超收敛定理是研究随机时变系统辨识算法有界收敛性的一个有效数学工具,它是鞅收益是在随机时变系统中的推广。文「1」用它证明了遗忘因子最小二乘算法参数估计误差的有界收敛性,但是文「1」假设系统的理各态遍历的,且协方差阵是用它的数学期望代替的,所得到的结果是近似的。而本文精确地给出了协方差阵的上下界,改进了文「1」的结果。 相似文献
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针对多元线性或非线性回归系统, 将耦合辨识思想与带遗忘因子有限数据窗辨识理论相结合, 提出一种耦合带遗忘因子有限数据窗递推最小二乘辨识算法. 该算法每次递推计算时既不涉及矩阵求逆运算, 又可以克服数据饱和现象, 因此, 该算法不仅计算效率高, 而且可以快速地跟踪时变参数, 获得精确的参数估计. 通过辨识基于多元模型的永磁同步电机参数的实例, 验证了所提出算法的有效性和实用性.
相似文献11.
不确定离散系统的最优鲁棒滤波 总被引:4,自引:0,他引:4
本文对一类含有范数有界参数不确定的离散线性系统的滤波问题进行了研究,了有限时域时变以及无限时域时不变两种情形,给出了一个对所有可容许参数不确定都能满足的估计误差方差上界,得到了使得该上界达到最小的最优鲁棒滤波器形式及其存在的充要条件,数值结果表明:当系统存在参数不确定时,本文所得到的滤波器优于标准的Kalman滤波器以及文(4)中的鲁棒滤波器。 相似文献
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In this paper, we consider the state estimation problem for linear discrete time‐varying systems subject to limited communication capacity which includes measurement quantization, random transmission delay and data‐packet dropouts. Based on transforming the three communication limitations into the system with norm‐bounded uncertainties and stochastic matrices, we design a robust filter such that, for all the communication limitations, the error state of the filtering process is mean square bounded. An upper bound on the variance of the state estimation error is first found, and then, a robust filter is derived by minimizing the prescribed upper bound in the sense of the matrix norm. It is shown that the desired filter can be obtained in terms of the solutions to two Riccati‐like difference equations which also provide a recursive algorithm suitable for online computation. A simulation example is presented to demonstrate the effectiveness and applicability of the proposed algorithm. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society 相似文献
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The stochastic Newton recursive algorithm is studied for system identification. The main advantage of this algorithm is that it has extensive form and may embrace more performance with flexible parameters. The primary problem is that the sample covariance matrix may be singular with numbers of model parameters and (or) no general input signal; such a situation hinders the identification process. Thus, the main contribution is adopting multi-innovation to correct the parameter estimation. This simple approach has been proven to solve the problem effectively and improve the identification accuracy. Combined with multi-innovation theory, two improved stochastic Newton recursive algorithms are then proposed for time-invariant and time-varying systems. The expressions of the parameter estimation error bounds have been derived via convergence analysis. The consistence and bounded convergence conclusions of the corresponding algorithms are drawn in detail, and the effect from innovation length and forgetting factor on the convergence property has been explained. The final illustrative examples demonstrate the effectiveness and the convergence properties of the recursive algorithms. 相似文献
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K. Warwick Y. -H. Kang R. J. Mitchell 《Soft Computing - A Fusion of Foundations, Methodologies and Applications》1999,3(4):200-205
The recursive least-squares algorithm with a forgetting factor has been extensively applied and studied for the on-line parameter
estimation of linear dynamic systems. This paper explores the use of genetic algorithms to improve the performance of the
recursive least-squares algorithm in the parameter estimation of time-varying systems. Simulation results show that the hybrid
recursive algorithm (GARLS), combining recursive least-squares with genetic algorithms, can achieve better results than the
standard recursive least-squares algorithm using only a forgetting factor. 相似文献