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maximum likelihood identification
相关语句
  最大似然参数估计
     Complex envelope digital filter is widely used i n computer simulation of modern wireless communication systems and radar systems . A frequency domain maximum likelihood identification method based on errors in variables model(EV model)for the complex envelope digital filter is presented .
     针对无线通讯系统和雷达系统的数字仿真中大量存在的复包络数字滤波器建模问题 ,提出了一种基于变量误差模型 (Errors- in- Variables Model,简记为 EV模型 )的频域最大似然参数估计方法。
短句来源
  “maximum likelihood identification”译为未确定词的双语例句
     Finally, the free to tumble experiment data of some aircraft in wind tunnel are processed using the maximum likelihood identification method, and the dynamic stability derivatives of the aircraft in the region of 0~180° ampliftude at M =4.0 and M =6.0 are gained.
     最后 ,采用最大似然辨识方法处理了某飞行器模型风洞自由翻滚实验数据 ,获取了该飞行器在M =4.0和M =6.0下 0°~ 1 80°振幅范围的动导数
短句来源
     The maximum likelihood identification of flexible structure in frequency domain
     挠性结构模型的频域极大似然法辨识
短句来源
     Maximum Likelihood Identification and Modeling of Digital Co mplex Envelope Filter
     复包络数字滤波器的最大似然辨识建模
短句来源
     In this paper, a nonlinear maximum likelihood identification method is developed.
     本文介绍一种非线性极大似然辩识方法。
短句来源
     In this paper which was in the background of some model antitank missle, adopting maximum likelihood identification method estimated the aerodynamic parameters of the missle, with using flight datas.
     本文以某型反坦克导弹为背景,利用导弹飞行试验数据,应用最大似然递推算法估算了该导弹的气动力参数。
短句来源
更多       
  相似匹配句对
     On Generalized Maximum Likelihood Estimation
     浅论“广义”最大似然估计
短句来源
     Totally Maximum Likelihood Estimation
     完全极大似然估计
短句来源
     is maximum.
     的影响极大。
短句来源
     APPLICATION OF MAXIMUM-LIKELIHOOD TO IDENTIFICATION OF UNDERWATER VEHICLE
     极大似然法在水下机器人系统辨识中的应用
短句来源
     IDENTIFICATION OF AERODYNAMIC COEFFICIENTS USING MAXIMUM LIKELIHOOD METHOD
     用极大似然法辨识轴对称飞行器的气动参数
短句来源
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  maximum likelihood identification
Maximum likelihood identification of neural point process systems
      


The partitioning estimation theory of Lainiotis is directly applied to parameter identification of general nonlinear time-varying dynamic system.A general partitioning identification algorithm (GPIA) was given without augmenting parameters as the system states. Some relationships and comparison of this method with maximum likelihood identification method and extended kalman filter algorithm are presented in analysis and numerical results. For example, it is applied to the identification of aerodynamic...

The partitioning estimation theory of Lainiotis is directly applied to parameter identification of general nonlinear time-varying dynamic system.A general partitioning identification algorithm (GPIA) was given without augmenting parameters as the system states. Some relationships and comparison of this method with maximum likelihood identification method and extended kalman filter algorithm are presented in analysis and numerical results. For example, it is applied to the identification of aerodynamic coefficients and control derivatives using simulating flight test data. The effects of the choice of measured variables and the sampling frequency and the design of manevuering flight test on the accuracy of identification are examined according to the simulating estimated results.

本文直接根据Lainiotis’s“分割”估计原理,不对系统状态增维,导出适用于一般非线性时变动力学系统的状态估计和参数辨识算法(GPIA)。并把它与极大似然法和广义卡尔曼滤波作参数估计的算法进行比较,初步分析了它们之间的相互关系。作为分割辨识算法的应用,本文根据模拟测量数据,用它对飞行器的气动系数进行辨识,且通过仿真计算说明测量变量的选取,机动飞行试验以及采样频率的设计对辨识精度的影响。

In this paper, a nonlinear maximum likelihood identification method is developed. It is based on system model in which both the state and control variables and the parameters to be estimated are dynamically nonlinear. The optimization problem is solved by the quasi-linearizing iteration algorithm.

本文介绍一种非线性极大似然辩识方法。该法以一般非线性动态系统为对象,从系统的非线性模型出发,按极大似然估计准则,经过拟线性化处理建立一个迭代的优化算法。将其应用于某高速飞机气动参数辩识,采用纵横向耦合的六自由度非线性运动方程和非线性气动模型作为飞机数学模型,由飞行试验数据同时估计飞机纵横向稳定性与操纵性导数。计算结果证实,这种算法处理非线性系统是有效的。

The method of Sputhenpura and Sinha [1] for identifying aircraft buffetting characteristics may often give inaccurate results. This paper goes back to Fisher's theory [3], on whichGoodwin's method is based, and presents a new algorithm that is better than that of Ref.[1] and avoids what we believe to be Goodwin's incorrect use of z-transformation. Theoretical study shows that due to the use of z-transformation, errors of Goodwin's gradientformulas are large. These errors can be much reduced with my new algorithm...

The method of Sputhenpura and Sinha [1] for identifying aircraft buffetting characteristics may often give inaccurate results. This paper goes back to Fisher's theory [3], on whichGoodwin's method is based, and presents a new algorithm that is better than that of Ref.[1] and avoids what we believe to be Goodwin's incorrect use of z-transformation. Theoretical study shows that due to the use of z-transformation, errors of Goodwin's gradientformulas are large. These errors can be much reduced with my new algorithm because robustness analysis shows that my new algorithm is very close to the ideal formulas of maximum likelihood identification method. Such an improvement is made possible, because in mynew algorithm, unlike GoodwinIS method, the errors of computation of gradients of unknown parameters do not contaminate other gradient computations. To get high numericalstability and computational efficiency, in my new algorithm, U-D factorization is used forthe propagation of error covariance matrices. Simulations show that my new algorithm possesses high robustness even when signal noise ratio is very low (such as- 8.541db) thus accurate estimates of unknown parameters can be obtained. For very low signal noise ratio,method of Ref. [1] is particticularly deficient in robustness. When mynew algorithm is used toidentify the buffetting characteristics of an aircraft, results obtained agree well with thoseobtained with off-line method.

指出了Goodwin递推极大似然法在辨识高阶系统时发散的必然性,并且给出了发散的原因。提出了一种梯度解耦的极大似然方法。该算法避免了各梯度计算误差的影响并提高了数值准确性。鲁棒性分析表明,新算法的梯度向量计算误差小,克服了Goodwin方法的辨识滞后问题。为了提高数值稳定性和计算收敛性,本文采用U一D分解技术并用渐消记忆等方法对算法进行补偿。仿真结果表明,新算法鲁棒性强、准确性好。此外,本文还讨论了飞机颤振特性辨识问题。

 
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