共查询到14条相似文献,搜索用时 171 毫秒
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针对含有未知时滞的多输入输出误差系统的时滞与参数辨识问题,提出一种基于辅助模型的正交匹配追踪迭代算法.首先,由于各输入通道的时滞未知,通过设定输入回归长度,对系统模型进行过参数化,得到一个高维的辨识模型,且辨识模型中参数向量为稀疏向量;然后,基于辅助模型思想和正交匹配追踪算法,在每次迭代过程中,对参数向量和辅助模型的输出进行交互估计,即利用正交匹配追踪算法获得参数向量的估计,再利用参数估计值计算辅助模型的输出,并用辅助模型的输出值代替信息向量中的不可测信息项以更新参数估计;最后,根据参数向量的稀疏特征,获得系统的时滞估计.所提出算法可以利用少量的采样数据信息同时获得系统参数和时滞的估计值.仿真结果表明了所提出算法的有效性. 相似文献
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MISO 系统基于正交匹配追踪算法的参数与时滞联合估计 总被引:1,自引:0,他引:1
在有限采样情况下, 研究具有时滞的多输入单输出受控自回归系统的参数辨识和时滞估计问题. 当采样次数少于未知变量数时, 描述系统的方程组是欠定的, 对其目标函数求解是NP-hard 问题, 传统方法无法有效辨识出系统参数. 受压缩感知理论的启发, 基于参数向量所具有的稀疏特性, 提出一种新的阈值正交匹配追踪算法辨识系统的参数和时滞. 仿真实验表明, 所提出的算法能在少量采样时有效地辨识系统参数、估计未知时滞, 同时验证了算法的有效性.
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利用提升技术可将非均匀采样非线性系统离散化为一个多输入单输出传递函数模型,从而将系统输出表示为非均匀刷新非线性输入和输出回归项的线性参数模型,进一步基于非线性输入的估计或过参数化方法进行辨识.然而,当非线性环节结构未知或不能被可测非均匀输入参数化表示时,上述辨识方法将不再适用.为了解决这个问题,利用核方法将原始非线性数据投影到高维特征空间中使其线性可分,再对投影后的数据应用递推最小二乘算法进行辨识,提出基于核递推最小二乘的非均匀采样非线性系统辨识方法.此外,针对系统含有有色噪声干扰的情况,参考递推增广最小二乘算法的思想,利用估计残差代替不可测噪声,提出核递推增广最小二乘算法.最后,通过仿真例子验证所提算法的有效性. 相似文献
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针对伪线性输出误差回归系统的辨识模型新息信息向量存在不可测变量的问题,首先通过构造一个辅助模型,用辅助模型的输出代替未知中间变量,推导得到的基于辅助模型的递推最小二乘参数估计算法计算量较大,但算法的辨识效果不佳。进一步采用估计的噪声模型对系统观测数据进行滤波,使用滤波后的数据进行参数估计,从而推导提出了基于数据滤波的递推最小二乘参数估计算法。仿真结果表明,所提算法能够有效估计伪线性回归线性输出误差系统的参数。 相似文献
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基于辅助模型的递推增广最小二乘辨识方法 总被引:4,自引:0,他引:4
针对有色噪声干扰的输出误差滑动平均系统, 将辅助模型与递推增广最小二乘算法相结合: 用辅助模型的输出代替辨识模型信息向量中的未知真实输出项, 用估计残差代替信息向量中的不可测噪声项, 从而提出了基于辅助模型的递推增广最小二乘辨识方法. 为了展示所提方法的特点, 文中还给出了经过模型变换的递推增广最小二乘算法. 理论分析和仿真研究表明, 提出的方法原理简单、计算量小, 可以给出高精度参数估计, 且能够用于在线辨识. 相似文献
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This paper is concerned with the design of a state filter for a time‐delay state‐space system with unknown parameters from noisy observation information. The key is to investigate new identification algorithms for interactive state and parameter estimation of the considered system. Firstly, an observability canonical state‐space model is derived from the original model by linear transformation for the purpose of simplifying the model structure. Secondly, a direct state filter is formulated by minimizing the state estimation error covariance matrix on the basis of the Kalman filtering principle. Thirdly, once the unknown states are estimated, a state filter–based recursive least squares algorithm is proposed for parameter estimation using the least squares principle. Then, a state filter–based hierarchical least squares algorithm is derived by decomposing the original system into several subsystems for improving the computational efficiency. Finally, the numerical examples illustrate the effectiveness and robustness of the proposed algorithms. 相似文献
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Jing Chen Bin Jiang Juan Li 《International Journal of Control, Automation and Systems》2018,16(1):150-157
This paper proposes a recursive least squares algorithm for a distributed parameter system with missing observations. By using the finite difference method, the distributed parameter system can be turned into a lumped parameter system. Then a missing output identification model based recursive least squares algorithm is derived to estimate the unknown parameters of the lumped parameter system. Furthermore, the parameters of the distributed parameter system can be computed by the estimated parameters of the lumped parameter system. The simulation results indicate that the proposed method is effective. 相似文献
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Mengting
Chen Feng Ding Rongming Lin Teng Yong Ng Yanliang Zhang Wei Wei 《国际强度与非线性控制杂志
》2020,30(15):6262-6280
》2020,30(15):6262-6280
This article is concerned with the parameter identification of output‐error bilinear‐parameter models with colored noises from measurement data. An auxiliary model least squares‐based iterative method is developed through the overparameterization model. It examines the difficulty of estimating the overparameterized vector, which usually presents a heavy computational burden in the identification process. To overcome this drawback, a parameter separation technique is introduced and the nonlinear model is reformulated as a refined identification model through eliminating the crossmultiplying terms. In this regard, a parameter separation least squares‐based iterative (PS‐LSI) algorithm is derived by avoiding estimating the redundant parameters. On the basis of the PS‐LSI algorithm, we derive a maximum likelihood least squares‐based iterative method to further improve the numerical accuracy. The identification is dependent on the formulation of a pseudolinear regression relationship, which contains two linear prefilters constructed from the system and noise models. The performance of this proposed method is confirmed by the numerical simulations as well as direct comparisons with other existing algorithms. 相似文献
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利用Householder变换推导出一个新的最小二乘估计的限定记忆快速递推算法(RHFM).
该算法具有运算量小,数值稳定性好、占用内存少的优点,可以用于各种静、动态模型的参
数估计. 相似文献