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1.Calculating the translation of beacon based on discrete Fourier transform(DFT) and maximum likelihood criterion,need solve two nonlinear equations,and the linearly approximated result shows that the total translation error is below 0.5 pixel,the translation error in X direction is 3.3%,and the translation error in Y the direction is 2.7%,can meet the need of deep space optical communication,so it is a feasible scheme.
基于离散傅里叶变换和极大似然方法对信标的平移进行计算,需要解一个非线性方程组,线性近似计算结果表明平移量总误差在0.5个像素以内,X方向的平移误差为3.3%,Y方向的平移误差为2.7%,可以满足深空通信的要求,因而是一种可行的方案。收藏指正
2.Anderson , Amemiva and Fujisawa et al scholars extended the growth curve model into the one with random effects and considered the likelihood ratio criterion (LRC) for its mean structure ,where the random vectors follow normal distribution on the assumptions that random effects and random errors are mutually independent[1][2] .
Anderson,Amemiva和Fujisawa等学者将GC模型推广为含有随机效应的增长曲线模型(The Growth Curve Model With Random Effects),并在观察矩阵服从正态分布的条件下作了关于均值的广义线性假设似然比检验。收藏指正
3.Modeling by Combining Maximum Likelihood Estimation with AIC Criterion
极大似然估计与AIC准则联合建模收藏指正
4.To get high numerical stability and computational efficiency,a new AIC criterion and recursive parameter estimation methods are developed which make both the maximum likelihood method and the optimal criterion for model choice of SISO system very simple. For MIMO system,a new AIC criterion is given and the computation formulas of the cost function is simplified. The results of aerodynamic model identification show that the new method can give accurate model structures of unknown system.
本文提出了一种有效的模型辨识新方法.为了提高数值稳定性和计算效率,本文给出了一种递阶最大信息量(AIC)新判据和参数估计新方法,使单输出系统的极大似然方法及模型辨识的优选判据计算量成倍减少.通过分析标量系统的AIC标准,本文进一步导出了多输出情况下的AIC标准,大大提高了计算效率.收藏指正
5.In the paper correlation identification, correlation filtering, criterion of AIC and maximum likelihood estimation are combined to detect the fault of a DC motor system.
本文将相关辨识、相关滤波、AIC准则与极大似然估计有机联系起来,并将应用于可控硅动态系统的故障检测。 本文提出的诊断过程如下:(1)加入激励信号为正常给定信号的0.1%~10%的逆重复M序列伪随机信号,以不影响系统正常运行为准.收藏指正
6.The problem of model fitting is one of the most important ones in statistical inference. Regression models in which random influence is normal distribution can be discriminated easily by AIC criterion. This paper discusses the entropy maximization principle, the least AIC criterion and the maximum likelihood function, lists some representative regression models, points out some problems which we must pay attention to in the procedure of model fitting.
模型的选优问题,是统计推断中最重要的问题之一,随机影响为正态分布的回归模型,用AIC准则可以进行很好地识别.针对这一问题,讨论了最大熵原理、最小AIC准则和最大似然函数的关系,列举了几种典型的回归模型,指出了模型识别过程中应注意的几个问题收藏指正
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