• Research of ar model spectrum estimation theory.

    AR模型谱估计理论研究。

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  • X spectrum processing method based on AR model was proposed.

    提出采用AR模型处理X光谱的方法。

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  • The order of the AR model can be determined by Final Predictio nError (FPE) criterion.

    而AR模型的阶次由最终预测误差(FPE)判据确定。

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  • Wiener three forecasting methods used to achieve the parameter estimation of AR model.

    利用维纳预测方法实现对AR模型的参数估计。

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  • Count method and ar model spectrum estimation method are adopted respectively to experiment.

    分别采用计数的方法和AR模型谱估计的方法进行实验。

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  • The Maximum Entropy method is a kind of parameter spectrum estimation method based on ar model.

    最大熵谱估计法是以AR模型为基础的一种参数谱估计方法。

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  • A method that ar model is applied to condition monitoring of deep hole processing is put forward.

    提出一种用时间序列ar模型进行深孔加工状态监测的方法。

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  • With the experiment the characteristic of the step factor and the order of ar model are researched.

    以实验的方法研究了步长因子的特征及模型阶数的选取。

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  • A fault diagnosis approach for rotor systems based on AR model and support vector machine is proposed.

    提出了基于AR模型和支持向量机的转子系统故障诊断方法。

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  • With bispectrum analysis, an ar model parametric bispectrum estimation is presented for radar target echoes.

    本文利用双谱分析方法,提出用非高斯ar模型对雷达目标回波信号进行参数化双谱估计。

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  • Objective To explore a better arithmetic of Doppler signal by discussing lag correlation method, ar model method.

    目的本文通过讨论处理多普勒信号的常用算法:自相关法,AR模型法,希望找到一种性能较好的算法。

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  • AR Model method is of definitive concept and less interference factors, thus its correction result is satisfactory.

    AR模型方法概念清楚,受干扰因素少,效果很好。

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  • We analyse statistics properties of ground clutters and study AR model of correlated clutters power spectral density.

    分析了地杂波统计特性,研究了相关雷达杂波功率谱特性的AR模型及其模拟方法。

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  • Second, a three-layer BP neural network was designed to classify the muscle movement of forearm with AR model coefficient.

    其次,设计了一个三层的BP神经网络,利用AR系数对手臂的各种肢体动作进行运动模式的分类。

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  • This paper USES several criteria to estimate the order of the AR model and the influence for the distance measurement outcomes.

    主要探讨在这种方法中利用不同的定阶准则建立相应的AR模型及其对距离测度结果的影响。

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  • Ar model based distance measurements with EEG signals are well reliable for the injury detection of the central nervous system.

    基于AR模型的EEG信号距离测度方法可以较好地表征中枢神经系统损伤的情况。

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  • Secondly, the locally linear prediction and ar model is introduced for the chaos sequences have the character of local predictability.

    其次,根据混沌特性中的短时可预测性介绍了局部线性拟合和AR模型预测算法。

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  • After difference disposal is applied to vibration signals, AR model is applied to signals collected in course of deep hole processing.

    对所采集的深孔加工过程中的振动信号先进行差分处理,再进行AR建模。

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  • After the first water addition, we adopt AR model to predict the stable value of sand compactibility to shorten the time mixing the sand.

    第一次加水后,对型砂紧实率稳定值采用AR模型进行预测,以缩短型砂混制时间。

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  • Auto regressive (AR) modeling is widely used in signal processing. The coefficients of an AR model can be easily obtained with an LMS algorithm.

    自适应(AR)模型是数字信号处理中广为应用的一种模型,它的系数可以通过LMS算法求解。

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  • It shows that an ar model is difficult to predict a sudden surge, because errors and delay are too large to be ignored in multi-step predication.

    多步预测的误差和滞后现象显示AR模型难以预测突发性的喘振现象。

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  • The real signals have often non-stationary characteristic, so if we analyse these time series using AR model directly, we cant obtain design result.

    由于实际信号常常具有非平稳特征,直接应用AR模型进行时间序列分析,得不到理想的效果。

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  • Experiments on the method show that AR model spectrum has the advantage of heving a continuous smooth curve, by which the damage factor can easily be obtained.

    结果表明,AR模型谱具有连续光滑易于提取损伤因子等特点,应用于主动监测信号的定量表征是非常有效的。

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  • On the basis of traditional time series analysis and modeling methods, the thesis puts forward a new complete and simply identification method by using ar model.

    本文在传统时间序列分析建模方法的基础上,提出了用AR模型的新的完整而又简单的辨识方法。

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  • A linear ar model is set up to be applied in real-time flood forecasting. Two criterion AIC and BIC are used to decide the exponent number of the linear ar model.

    建立线性自回归模型,应用于洪水实时预报,并应用aic、BIC这两种准则以确定自回归模型的阶数。

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  • Through the data obtained by fieldwork, the paper forecasts the short-term traffic by three methods: moving-average method, index-smoothing method, AR model method.

    本文通过实地调查获取的交通流量数据,分别采用移动平均法、指数平滑法、AR模型法三种交通流预测方法进行短时交通流量预测。

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  • The ARMA model was used to describe the prior distribution of observed discharge and the ar model was adopted to simulate the likelihood function of forecasting error.

    该模型采用ARMA模型描述实测流量的先验分布,采用AR模型模拟预报残差的似然函数,并假定先验分布和似然函数均服从正态分布。

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  • According to the structure characteristics of a time varying time series model, a new recursive parameter estimation algorithm of the time varying ar model is proposed.

    根据对一类时变时间序列模型结构特点的研究,提出了一种时变ar模型的递推参数估计算法。

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  • This paper proposes the prediction method of time series based on AR model and forecasts the development trend of weighted length of fiber for hydrocyclone by this method.

    以时间序列为理论基础,利用时间序列分析方法对水力旋流器的筛分性能建立了AR模型,并依据该模型对纤维重均长度变化趋势进行了预测分析。

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  • This paper proposes the prediction method of time series based on AR model and forecasts the development trend of weighted length of fiber for hydrocyclone by this method.

    以时间序列为理论基础,利用时间序列分析方法对水力旋流器的筛分性能建立了AR模型,并依据该模型对纤维重均长度变化趋势进行了预测分析。

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