共查询到18条相似文献,搜索用时 171 毫秒
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针对一类耦合参数多变量系统, 提出一种耦合多新息随机梯度方法. 通过该方法进行参数辨识并对该方法进行性能分析. 该方法的基本思路在于利用历史新息中包含的信息, 将耦合随机梯度算法中的新息项扩展为多新息向量, 从而提升耦合随机梯度算法中单个子系统的辨识效果. 仿真结果表明, 通过增加新息长度可以提升辨识结果的收敛速度和精度.
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对于有色噪声干扰的输出误差多输入单输出(MISO)系统,常规的递推最小二乘辨识方法给出的参数估计是有偏的.为了提高随机梯度辨识方法的收敛精度和速度,用辅助模型的输出代替辨识模型信息向量中的未知不可测变量,推导出其辅助模型增广随机梯度辨识算法;再引入新息长度扩展标量新息为新息向量,提出了基于辅助模型的MISO系统多新息增广随机梯度辨识算法.所得算法在每一次的迭代中不仅使用了当前数据和新息,而且使用了过去数据和新息,提高了参数估计精度和收敛速度.仿真例子验证了算法的有效性. 相似文献
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针对风力机桨距系统故障,提出一种基于观测器的多新息随机梯度辨识算法的故障诊断方法.多新息随机梯度辨识算法通过扩展新息长度能够改进随机梯度辨识算法的估计精度,根据系统的规范状态空间模型,结合状态观测器可以实现系统状态和参数的交互估计.将桨距系统模型转换为可辨识的状态空间模型,依据桨距系统故障会引起系统参数变化的特点,采用所提出的算法对系统状态和参数进行估计,将桨距系统故障诊断问题转化为系统状态和参数估计问题.仿真结果表明,所提出的方法能够有效诊断桨距系统故障. 相似文献
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对于有色噪声干扰的输出误差多输入单输出(MISO)系统,常规的递推最小二乘辨识方法给出的参数估计是有偏的。为了提高随机梯度辨识方法的收敛精度和速度,用辅助模型的输出代替辨识模型信息向量中的未知不可测变量,推导出其辅助模型增广随机梯度辨识算法;再引入新息长度扩展标量新息为新息向量,提出了基于辅助模型的MISO系统多新息增广随机梯度辨识算法。所得算法在每一次的迭代中不仅使用了当前数据和新息,而且使用了过去数据和新息,提高了参数估计精度和收敛速度。仿真例子验证了算法的有效性。 相似文献
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基于辅助模型的多新息广义增广随机梯度算法 总被引:7,自引:1,他引:6
将辅助模型辨识思想与多新息辨识理论相结合,利用系统可测信忠建立一个辅助模型.分别用辅助模型输出和噪声估计值代替辨识模型信忠向量中未知真实输出变量和不可测噪声项,并引入新忠长度扩展标量新息为新息向量,提出了Box-lenkins模型的辅助模型多新忠广义增广随机梯度辨识方法.所提出方法重复使用系统数据,能够改善参数估计精度,加快算法的收敛速度. 相似文献
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针对多变量输出误差系统的模型辨识问题,借助辅助模型思想推导出其随机梯度辨识算法;由于该算法的收敛速度慢,为了提高收敛速度,将算法中的新息向量扩展成新息矩阵,得到基于辅助模型的多新息随机梯度辨识算法;辅助模型多新息算法使用新息矩阵对参数进行校正估计,该新息矩阵不仅包含了当前时刻的新息向量,还包含过去多个时刻的新息向量,因而,与辅助模型随机梯度算法和增广随机梯度算法相比,该算法具有更快的收敛速度;一个二输入二输出的仿真例子证明了所提出的算法的确具有更快的收敛速度. 相似文献
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It is well-known that the stochastic gradient (SG) identification algorithm has poor convergence rate. In order to improve the convergence rate, we extend the SG algorithm from the viewpoint of innovation modification and present multi-innovation gradient type identification algorithms, including a multi-innovation stochastic gradient (MISG) algorithm and a multi-innovation forgetting gradient (MIFG) algorithm. Because the multi-innovation gradient type algorithms use not only the current data but also the past data at each iteration, parameter estimation accuracy can be improved. Finally, the performance analysis and simulation results show that the proposed MISG and MIFG algorithms have faster convergence rates and better tracking performance than their corresponding SG algorithms. 相似文献
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Parameter estimation for nonlinear systems by using the data filtering and the multi-innovation identification theory 总被引:1,自引:0,他引:1
Yawen Mao 《国际计算机数学杂志》2016,93(11):1869-1885
For Hammerstein output-error autoregressive systems, a decomposition based multi-innovation stochastic gradient (D-MISG) identification algorithm and a data filtering based multi-innovation stochastic gradient (F-MISG) identification algorithm are derived by means of the key-term separation principle and the multi-innovation identification theory. The D-MISG algorithm uses the decomposition technique to transform a Hammerstein system into two subsystems and requires less computational cost, and the F-MISG algorithm uses a linear filter to filter the input-output data and has a higher estimation accuracy for larger innovation lengths. The simulation results show that the proposed two algorithm can give satisfactory parameter estimates. 相似文献
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Jian Yao Yanping Huang Zhicheng Ji 《International Journal of Control, Automation and Systems》2013,11(6):1170-1176
An output nonlinear Wiener system is rewritten as a standard least squares form by reconstructing the input-output items of its difference equation. Multi-innovation based stochastic gradient (MISG) algorithm and its derivate algorithms are introduced to formulate identification methods of Wiener models. In order to increase the convergence performance of stochastic gradient (SG) algorithm, the scalar innovation in SG algorithm is expanded to an innovation vector which contains more information about input-output data. Furthermore, a proper forgetting factor for SG algorithm is introduced to get a faster convergence rates. The comparisons of convergence performance and estimation errors of proposed algorithms are illustrated by two numerical simulation examples. 相似文献
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This paper studies the data filtering‐based identification algorithms for an exponential autoregressive time‐series model with moving average noise. By means of the data filtering technique and the hierarchical identification principle, the identification model is transformed into three sub‐identification (Sub‐ID) models, and a filtering‐based three‐stage extended stochastic gradient algorithm is derived for identifying these Sub‐ID models. In order to improve the parameter estimation accuracy, a filtering‐based three‐stage multi‐innovation extended stochastic gradient (F‐3S‐MIESG) algorithm is developed by using the multi‐innovation identification theory. The simulation results indicate that the proposed F‐3S‐MIESG algorithm can work well. 相似文献
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阐述了非均匀采样方案,推导了非均匀多率采样系统的状态空间模型,进一步获得了对应的传递函数模型.为解决辨识模型信息向量中存在未知变量的问题,使用辅助模型技术,用辅助模型的输出代替系统的未知变量,进而提出了非均匀采样数据系统的辅助模型随机梯度辨识算法.为了提高算法收敛速度和改善参数估计精度,在算法中引入遗忘因子,给出了相应的辅助模型带遗忘因子随机梯度算法.仿真结果表明,引入遗忘因子后,算法的收敛速度加快,参数估计精度提高. 相似文献
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Feng Ding 《Digital Signal Processing》2010,20(4):1027-1039
This paper considers connections between the cost functions of some parameter identification methods for system modelling, including the well known projection algorithm, stochastic gradient (SG) algorithm and recursive least squares (RLS) algorithm, and presents a modified SG algorithm by introducing the convergence index and a multi-innovation projection algorithm, a multi-innovation SG algorithm and a multi-innovation RLS algorithm by introducing the innovation length, aiming at improving the convergence rate of the SG and RLS algorithms. Furthermore, this paper derives an interval-varying multi-innovation SG and an interval-varying multi-innovation RLS algorithm in order to deal with missing data cases. 相似文献