将板形检测信号视为动态时间序列，运用自适应滤波理论，建立了一种通用的板形检测信号除噪方法。
Flatness detection signal was looked on as dynamic time series and by the theory of adaptive filtering a general method was developed to remove the harmful noise from the flatness detection signal.
动态时间序列周期分析预测模型是从数理统计的角度对值为连续型的时间序列进行分析，发现规律，从而成功预测未来。
The Dynamic time series period analysis and prediction model analyses a serial-typed time series from the point of statistics, finding out the law. thereby succeeding in predicting the future.
传递函数———噪声模型是一类多变量时间序列模型，它在表达系统动态影响机制方面有着独到的优势。
The transfer function-noise model is a kind of multivariate time series model which has much advantage in expressing dynamic mechanism of a system.
通过时间序列分析建立反映切削状态的数学模型，从动态数据中凝聚信息，构成用于判别的特征向量。
By time series analysis, we build models depicting the cutting tool states, coacervate information from dynamic date and construct feature vectors for discrimination.
时间序列分析是动态数据分析的重要方法，在多学科领域中得到广泛的研究和运用。
Time series analysis is an important method of the dynamic data analysis which has extensive researches and usages in many subject fields.
详细阐明了时间序列的基本思想、几种基本时序模型和时序动态特征，讨论分析了如何进行模型识别、模型参数计算和模型的定阶。
The basic idea and some kinds of the common time series models and the development characteristics of time series are explained in detail.
本文对数据成时间序列的动态决策表，用增量式算法提取决策表的规则模型。
In this paper, we extract rules of the decision table by an incremental algorithm for the dynamic decision table of the time series data.
根据其时间序列，建立线性神经网络模型，并将其用于地下水流量的动态预测。
Based on the time series, a model of linear artificial neural network is set and used for dynamic prediction of discharge of groundwater.
最后给出了用于时间序列分析的动态贝叶斯网络的实例。
In the last, we give an example of dynamical Bayesian networks for time series data analysis.
动态贝叶斯网络(DBN)，以其扩展性和对时间序列的强大描述、推导和学习能力，逐渐被应用于连续语音识别中。
Dynamic Bayesian Network (DBN), because of extensibility, powerful description, inference and learning abilities for the time series, being used in the speech recognition.
最后，我们提出了结合时间序列表达数据和静态数据来构建动态调控网络的方法。
Finally, we present methods for combining time series expression data with static data to reconstruct dynamic regulatory networks.
研究了离散时间动态系统混沌序列的统计特性。
This paper analysed the statistic property of chaotic sequence generated by a widely studied discrete time dynamic system, Logistic_Map.
由于对数据的分析不仅关注某一时点，而且还要在时间序列上进行定量分析，故采用了静态分析与动态分析相结合的方法。
Because the data analyzing is not focused only on one moment, but also the analysis is carried out on the data by time sequence, static state research and the dynamic state research is combined.
然后计算训练序列与标准步态模型之间的动态时间规整距离，确定阈值。
Thresholds are determined by dynamic time warping (DTW) distance between training sequences and standard model.
在路基填筑施工过程中，根据沉降观测数据用时间序列分析方法建立等维信息动态预测模型。
During the filling construction of the roadbed the total settlement value could be predicted by using time series equal interval prediction model of recent information.
采用动态时间归整(DTW)算法对语音信号进行特征参数序列比较并识别出结果。
The speech recognition system adopts dynamic time warping (DTW) algorism to compare the characteristic parameters of speech signal with each other and recognize the result of speech signal.
近年来GARCH模型被广泛地用于对变动频率很高的金融时间序列建模，它能较好地抓住此类时间序列的动态特征。
At present, GARCH type models have been employed to model these high frequency financial time series due to their ability to capture the dynamic characteristics.
因此，如何有效地刻画金融时间序列波动的动态行为一直是金融计量学研究的热点问题。
Therefore, how to describe the dynamic behavior of the financial time series' fluctuation well is always a hot research point in Financial Econometrics.
构造了元件故障向量、维修向量、故障频率序列和修复序列，并利用其运算描述了元件的工作、故障、维修和修复过程，从而计算出在事后维修方式和维修时间分布影响下的元件动态可靠性指标：故障频率，有效度。
With them the process of units work, failure, repair and renovation is described, sequentially the dynamic failure and availability of the units can be computed under the influence of the repair time.
提出用起伏型时间序列法对数字图书馆图书流量进行动态分析。
The Analysis of wave type time series is a new method, which is used to simulate the data of the digital library book flow in a university library in the paper.
最常使用的五个模型是石油期货价格、回归结构模型、时间序列分析、贝叶斯自回归模型和动态随机一般均衡图。
The five models used most often are oil futures prices, regression-based structural models, time-series analysis, Bayesian autoregressive models and dynamic stochastic general equilibrium graphs.
理论分析和仿真结果表明，该算法对基于趋势表示的子序列搜索在时间和空间上都具有更优的性能，适用于时间序列的动态特征分析。
Theoretic analysis and simulation indicate that the algorithm has better performance for sub-trend searching in temporal and space, and is useful in time series dynamic feature analysis.
采用多元时间序列分析的方法，对油水井动态特性进行了分析，建立了油井含水率的多因素预测模型。
Multivariate time series method is used to analyze the performance of oil production and injection Wells. A multi factor model for predicting the water cut in oil well is presented.
将时间序列的季节模型用于人民胜利渠古黄河背河洼区、漫滩区月平均地下水埋深动态建模之中。
The seasonal model of time series is used for modelling the monthly average groundwater table of the depression area of the old yellow river in the People' s Victory Canal District.
时间序列分析所论及的就是对这种依赖性进行分析的技巧，这要求对时间序列数据建立随机动态模型。
What time series mention is the analytical technique to this kind of dependence. This requires found stochastic and dynamic models of time series.
时间序列分析是统计学的一个重要分支，灰色系统理论是一种动态趋势预测理论。
Time series analysis is a branch of statistics and widely used in trend prediction.
通过建立系统的数学模型，求出了动态特性方程，计算出这三种输入整形器脉冲序列的幅值和时间。
To derive mathematical model, dynamic characteristic equations are obtained and the pulse amplitude and time of input shaper was calculated.
通过建立系统的数学模型，求出了动态特性方程，计算出这三种输入整形器脉冲序列的幅值和时间。
To derive mathematical model, dynamic characteristic equations are obtained and the pulse amplitude and time of input shaper was calculated.
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