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。
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。
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。
The method utilizes an AR model to approach the frequency response of the clutter spectrum "inverted" ideal band-pass filter, and then implements the design of optimal FIR filter.
该方法利用AR模型逼近杂波谱“倒置”理想带通滤波器的频率响应，实现了最优FIR滤波器的设计。
It explores the second order statistics of fading envelopes influenced by nonisotropic scattering environments with light-of-sight (LOS), and USES an AR model for channel simulation.
在研究接收信号(含直射和方向性散射)复包络二阶统计特性的基础上，采用AR模型对信道进行仿真。
An. example of a set of gas data sampled from a certain foul coal mine is investigated, and an AR (3) model is established.
本文以一组采自某瓦斯矿的瓦斯数据为例，用时间序列方法进行了分析并建立了AR(3)模型。
This paper provides a method for the ar order estimation of two dimension ARMA model, and improves an ar parameters estimation method on the basis of autocorrelation function estimation method.
本文正是针对该点，提出了一种二维arma模型的初步定阶方法，同时对AR参数的自相关函数估计方法进行了改进，在此基础上得到了功率谱估计算法。
The compressor exit total pressure is adopted as a characteristic signal to detect surges. An ar (autoregressive) model is constructed with the time series analysis method.
以压气机出口总压作为喘振检测特征信号，采用时间序列分析方法建立了AR模型。
Based on this conclusion, the authors set up an AR-GARCH model of issuing scale of national debt. This model has high accuracy and strong capacity of prediction.
据此基本结论建立的国债发行规模的AR - GARCH模型精度高，有很强的预测功能。
Then, an improved AR(1) model is proposed. Through this new model, the response time of a DNS server can be dynamic predicted using previous response time series.
同时，基于已有的AR（1）模型，提出了一种改进型AR（1）自回归模型，该模型能够利用历次服务器响应时间构成的时间序列，采用动态预测的方法来预测服务器响应时间。
Then, an improved AR(1) model is proposed. Through this new model, the response time of a DNS server can be dynamic predicted using previous response time series.
同时，基于已有的AR（1）模型，提出了一种改进型AR（1）自回归模型，该模型能够利用历次服务器响应时间构成的时间序列，采用动态预测的方法来预测服务器响应时间。
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