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平均参数估计
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  average estimation
     By augmenting a certain number of recent linear local models,a rolling multiple model weighted average estimation algorithm is presented for tracking the fast timevarying model parameters.
     为了跟踪快速变化的模型参数 ,利用最新的多个线性局部模型进行外推 ,提出了一种滚动多模型加权平均参数估计算法。
短句来源
     A rolling multiple model weighted average estimation algorithm is presented for tracking the fast time varying model parameters.
     为了跟踪快时变参数 ,提出了一种滚动多模型加权平均参数估计算法。
短句来源
  相似匹配句对
     Parameter Estimation of Partial Differential Equations
     偏微分方程的参数估计
短句来源
     Mean Consistency for a Semiparametric Regression Model
     半参数回归模型估计平均相合性
短句来源
     Techniques of Parameter Estimation in Harmonic Means Combining Forecasts
     调和平均组合预测中的参数估计技术
短句来源
     Parameter Estimation of Distributed Sources
     分布式目标参数估计
短句来源
     ESTIMATION OF THE MEAN DEVIATION OF RATIONAL FUNCTION
     有理函数的平均偏差估计
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  average estimation
Testing the new model for radiometric measurements showed that the average estimation error for 10 varieties under early rice conditions was less than 1%.
      
The average estimation error from patient data loop control of nine patient treatments.
      
The average estimation error from patient data analysis of 21 sites at which temperature was independently measured (three per patient) was 0·0°C, with a standard deviation of 0·8°C.
      
The average estimation errors were 7% and 9% when two and three synthetic radiographic images obtained at different x-ray tube settings were used.
      
An extension of this multi-point conditional average estimation is presented in this paper in the form of a pseudo-dynamic reconstruction of the flow field.
      
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Asimple and practical linear current local model is proposed for identification and prediction of nonlinear timevarying systems with unknown and immeasurable deterministic disturbance.The existing theorem of the local model and its proof are descrbbed.By augmenting a certain number of recent linear local models,a rolling multiple model weighted average estimation algorithm is presented for tracking the fast timevarying model parameters.Simulation results conform the effectiveness of the current local model and...

Asimple and practical linear current local model is proposed for identification and prediction of nonlinear timevarying systems with unknown and immeasurable deterministic disturbance.The existing theorem of the local model and its proof are descrbbed.By augmenting a certain number of recent linear local models,a rolling multiple model weighted average estimation algorithm is presented for tracking the fast timevarying model parameters.Simulation results conform the effectiveness of the current local model and the estimation algorithm.

针对包含未知和不可测量的确定性扰动的非线性时变系统的辨识和预测 ,提出了一种简便实用的线性化即时局部模型 ,给出并证明了这种即时模型的存在性定理。为了跟踪快速变化的模型参数 ,利用最新的多个线性局部模型进行外推 ,提出了一种滚动多模型加权平均参数估计算法。仿真结果表明了这种即时局部模型和参数估计算法的可行性

A simple and practical incremental linear regression model in one unknown is proposed for controlled systems. A rolling multiple model weighted average estimation algorithm is presented for tracking the fast time varying model parameters. Applying intelligent approach to parameter estimation and system control lets the estimated parameters more reliable and leads to a multi mode control way. Adaptive inverse control based on this model and conventional control play a leading and an auxiliary role respectively...

A simple and practical incremental linear regression model in one unknown is proposed for controlled systems. A rolling multiple model weighted average estimation algorithm is presented for tracking the fast time varying model parameters. Applying intelligent approach to parameter estimation and system control lets the estimated parameters more reliable and leads to a multi mode control way. Adaptive inverse control based on this model and conventional control play a leading and an auxiliary role respectively in this control way. Simulation results show that this control way is effective to nonlinear and time varying systems.

为受控系统提出了一种简便实用的增量式一元线性回归模型。为了跟踪快时变参数 ,提出了一种滚动多模型加权平均参数估计算法。在参数估计和系统控制的过程中运用智能技术 ,使估值更可靠 ,并形成了以基于该模型的自适应逆控制为主、常规控制为辅的多模态控制方式。仿真结果表明 ,这一控制方式对于控制非线性和时变系统非常有效。

 
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