自回归方程

词组短语

autoregression equation

与自回归方程相关的网络例句 [注：此内容来源于网络，仅供参考]

Based on the autoregression equation for the standard variation of the residual errors obtained by ordinary regression analysis, the least square estimates and the maximum likelihood estimates for the regression, the autoregression and the moving average coefficients are derived.

提出一种异方差回归－时序模型，通过建立回归分析残差的标准差自回归方程，给出回归系数、自回归系数和滑动平均系数的最小二乘估计和极大似然估计。

In order to improve the high-performance concrete quality of longer brittleness, lower tensile strength, particularly the less descending branch in stress-strain curve compared with that of ordinary concrete. The breakthrough of this thesis lies in the mixture of short carbon fiber. Four factors such as water-binder ratio, volume ratio of short carbon fiber and blending value of high efficiency water-reducing admixture were selected as variable. Experiment was carried out by utilizing uniform design. Regression equation was established by regression analysis of the compression strength and the split tensile strength separately utilizing SPSS software. The process of optimization was obtained with the aid of MATLAB. Finally the optimized ratio was acquired.

为了改善高强混凝土由於水泥自缩、湿涨造成的脆性大、抗拉强度低，特别是应力－应变曲线的下降段不及普通混凝土的特点，本文以掺入短碳纤维为突破口，选取水胶比、砂率、短碳纤维的体积比、高效减水剂掺量四个因素作为变量，利用均匀设计安排试验，用SPSS软件对抗压强度和劈拉强度分别进行回归分析而得到回归方程，用MATLAB进行优化处理，进而求出最优配比。

Which indicates the regression equation 6 is plagued by auto correlation and we can not trust on the estimated t ratios, and coefficient of determination.

这表明回归方程6所困扰自相关，我们不能信任比率估计吨，并决定系数。

A important result is the one-order expression of AR Yt = DYt-1 + E, from paralleling a high-order differential equation transformation into a one-order differential equation system, the one-order expression exposes that the AR is only a certain more-multivariable power series processAnd, if a process is described as an AR, the sufficient and necessary condition is the spectrum norm A of the coefficient matrix D less than one.

作者用高阶微分方程化一阶微分方程组的方法，获得多元弱平稳序列p阶自回归模型的一步滑动平均表达式，证明了AR的是一个更高维的幂级数的线性过程，从而，说明了AR关于序列依概率成立的充要条件是：该模型更高维的幂级数的线性过程的表达式中系数矩阵D的谱范数λ＜1。

objective to study the correlation among psychosomatic health factors,depression,anxiety and sleep status in college students,to analyze influence factors in sleep status.methods with questionnaire opened in-vestigation,200college students were evaluated using cornell medical index,self-rating depression,self-rating anxiety scaleand pittsburgh sleep quality index.stepwise regression analysis was used.re-sults total score of sds was35.55±7.8,and sas was0.48±0.9.each score of cmi was higher than normal range,the highest was digestion system,next was respiration system,then,fatigue,eyes and ears,anxiety,sensitivity,tension,maladjustment in turn.total average score of psqi was6.32±3.6,ordinary sleep were115cases（57.5%）,good and bad sleep were42（21%）and43（21.5%）cases respectively.influence factors in total score of psqi were sas,cmi,respiration system,anxiety,past healthy,digestion system,fatigue,sensitivity,sds and malad-justment in turn,using stepwise regression analysis,total score of psqi as dependent variable,each factor score of cmi,total score of sds and sas as independent variables.conclusion problems of psychosomatic health,anxiety and depression could both lead to sleep disorder.

目的 研究在校大学生心身健康因素及抑郁焦虑等与睡眠状况的相关性，并对影响睡眠状况的有关因素进行分析。方法采用问卷式的开放性研究，对在校的200名大学生进行康奈尔心身健康问卷、自评抑郁量表、焦虑自评量表和匹兹堡睡眠质量量表的测定，采用逐步回归分析方法。结果 sds总分为35.55±7.8、sas总分为0.48±0.9，cmi评分：消化系统最为严重评分为2.52，其次为呼吸系统、疲劳感、眼和耳、焦虑、敏感、紧张、不适应等，评分均高于正常常模。psqi总均分为6.32±3.6，一般睡眠有115例占57.5%，睡眠质量较好42例占21.0%，睡眠质量较差43例占21.5%。评估对睡眠状况的影响程度，以psqi总分为因变量，选择cmi各因子分和sds、sas总分作为自变量，进行多因素逐步回归分析，进入方程的因素依据标准化偏回归系数，影响psqi总分的因素依次为sas总分、cmi总分、呼吸系统、焦虑、既往健康、消化系统、疲劳感、敏感、sds总分、不适应（f=226.8；p.01；r=0.73）。结论心身健康问题和焦虑抑郁可导致睡眠障碍。

In this paper, the generator model of constant transient voltage and the invariableness impedance model was used, and the saliency of generator is included; through the analysis of the characteristic of the network equation calculation in the transient stability analysis, a new node ordering algorithm is proposed. Better initial value can accelerate the convergence rate of the networking equations, reduce the number of iterations, thus to cut down the amount of calculating time.

本文首先建立了适合于暂态稳定分析的简单模型，其中发电机采用E_q恒定模型，考虑了发电机的暂态凸极效应，负荷采用恒定阻抗模型；研究了电力系统的暂态稳定分析方法，指出目前数值积分暂态稳定计算方法中存在的不足，主要是网络方程的求解速度过慢；分析了暂态稳定计算中网络方程的特点，将稀疏技术应用于网络方程的求解，提出了一种新的节点编号方法——最小度最小有源节点道路集算法，减少了网络方程的计算量；在进行交替迭代求解时，将预测算法应用于每一积分步网络方程功角和电压的初值估计当中，应用自回归算法和广义延拓算法，期望给出一个更有效的电压和功角初值方案，来加快方程的收敛过程，减少方程迭代的次数，以提高计算速度。

By the modem time series analysis method, based on the ARMA innovation model, under the linear minimum variance optimal information fusion criterion, three distributed fusion steady-state optimal Kalman filters, predictors and smoothers weighted by matrices, scalars, and diagonal matrices are presented for multisensor systems with correlated input and observation noises, and with correlated observation noises. The Lyapunov equations and formulas of computing local filtering, predicting and smoothing error variances and covariances are given, which are applied to compute optimal weights. The corresponding three distributed fusion Wiener state estimators are also presented.

应用现代时间序列分析方法，基于自回归滑动平均新息模型，在线性最小方差最优信息融合准则下，对于带相关输入噪声和观测噪声和带相关的观测噪声的多传感器系统，提出了按矩阵加权、按标量加权和按对角阵加权的三种分布式融合稳态Kalman滤波器、预报器和平滑器，其中提出了局部滤波、预报和平滑估值误差方差阵和协方差阵的Lyapunov方程和计算公式，它们被用于计算最优加权，也提出了相应的三种分布式融合Wiener状念估值器。

与自回归方程相关的网络解释 [注：此内容来源于网络，仅供参考]

first-order autocorrelation coefficient：一阶自相关系数

first-order aberration coefficient 初级像差系数 | first-order autocorrelation coefficient 一阶自相关系数 | first-order autoregressive equation 一阶自回归方程

autoregression equation：自回归方程

autoregression 自回归 | autoregression equation 自回归方程 | autoregressive process 自回归过程

autoregression：自回归

单位根检验方程为:Box和Jenkins于1976年提出了单整自回归移动平均(ARIMA)模型,ARIMA(p,d,q)实际上包括了自回归(Autoregression)、单整(Integration)、移动平均(Moving-average)三方面的内容.

autoregressive process：自回归过程

autoregression equation 自回归方程 | autoregressive process 自回归过程 | autoregressive transformation 自回归变换

Systolic Array：脉动阵列

处理速度的脉动阵列(systolic array)结构来实现QR-RLS算法.通过建立以最优干扰抑制器权矢量为状态矢量的状态方程和以期望信号的多线性回归模型为观测方程得到实现最优干扰抑制的Kalman滤波.以上自适应方法的不足是需要期望信号.本文通过约束矩阵的零空间矩阵将约束MMOE问题转化为低维无约束MMSE问题,