autoregressive moving average 中文意思是什麼

autoregressive moving average 解釋
自回歸滑動平均
  • autoregressive : 自回歸的
  • moving : n. 1. 活動,移動；煽動,感動。2. 〈pl. 〉〈口語〉電影。adj. 1. 動的；移動的。2. 使人感動的,動人的。3. 主動的，原動力的。
  • average : n 1 平均，平均數。2 一般水平，平均標準。3 【商業】海損；海損費用；（給領航的）報酬。adj 1 平均的...
  1. Moreover, special aspects of self - similar traffic are summarized. for long - range dependent traffic, two prediction models are given and discussed the prediction results can be applied to reduce loss ratio in allocation of memories in network nodes. the first model is farima ( fractional autoregressive integrated moving average )

    根據自相似業務流的長相關特性，本文重點討論了兩種數學模型，目的是用這兩種模型對自相似業務流進行預測，進而根據預測結果對計算機網路節點的存儲器資源進行合理的分配，使得丟失率達到最小。
  2. This system adopts cumulatively autoregressive moving average model [ arima ] of time series method and modified model gm ( 1, 1 ) of grey system, makes a local load forecasting modeling through the integration of the above two models and also preprocesses the daily load during the sudden change of climate, thus greatly improving the forecast accuracy. the practical operation indicates that the model is reasonable and easy to operate with complete function

    本系統在經過反復試算后，在演算法上採用了時間序列法的累積式自回歸動平均模型（ arima ）與灰色系統中的gm （ 1 ， 1 ）改進模型，並將兩種模型組合用於該地區負荷預報建模，另外還對氣候急變日負荷進行了預處理，大大提高了預報準確度。
  3. Two - stage algorithms of parameter estimation for the autoregressive moving average ( arma ) models are presented, which are called two - stage recursive least squares algorithm ( 2 - rls ) and recursive least squares - pseudoinverse algorithm ( rls - pi )

    本文提出了自回歸滑動平均（ arma ）模型的兩段參數估計演算法：兩段遞推最小二乘演算法（ 2 - rls ）和遞推最小二乘-偽逆演算法（ rls - pi ） 。
  4. According to the needs of gps / sins integrated navigation algorithm, the error models of gps and sins are studied respectively. the autoregressive ( ar ) models and autoregressive moving average ( arma ) models of gps positioning error are established based on the analysis of the properties of static gps positioning error data. and the neural network method to determine the ar model parameters is given

    根據gps / sins組合導航演算法的需要，分別對gps和捷聯系統的誤差模型進行了研究，在對gps靜態定位誤差數據特性分析的基礎上，建立了gps定位誤差的自回歸（ ar ）模型和自回歸滑動平均和（ arma ）模型，並用神經網路方法確定了ar模型參數。
  5. Autoregressive and moving - average time - series processes

    自回歸和移動平均時間序列過程
  6. Autoregressive integrated moving average model

    自回歸積分滑動平均模型
  7. Autoregressive moving average model

    自回歸滑動平均模型
  8. Compared to the traditional parameter - fixed autoregressive moving average ( arma ) method, the mep algorithm is adaptive and capable of tracking rtt dynamics rapidly

    與傳統的參數固定的自回歸滑動平均（ arma ）方法比較， mep演算法是自適應的並能夠迅速動態地跟蹤rtt 。
  9. We consider a causal, stationary, autoregressive, moving average [ arma ( p, q ) ] process with heavy tailed noise variables. we develop the definition of the inverse autocorrelation function, and obtain the g - spectral estimator of the inverse autocorrelation function

    本文基於噪聲序列具有重尾分佈的因果、平穩自回歸滑動平均[ arma （ p ， q ） ]過程，給出了其逆自相關函數的定義，並且給出了逆自相關函數的g -譜估計。
  10. Based on the classical least squares method ( rls ) in system identification, the several new identification algorithms of parameter estimation for the autoregressive moving average ( arma ) model, are presented. they include univariable and multivariable two - stage recursive least squares - recursive extended least squares ( rls - rels ) and two - stage recursive least squares - pseudo - inverse ( rls - pi ) algorithms

    本文在系統辨識經典的最小二乘法（ rls ）的基礎上，提出了自回歸滑動平均（ arma ）模型參數估計的一些新的辨識演算法，它包括單變量和多變量兩段遞推最小二乘?遞推增廣最小二乘（ rls ? rels ）演算法和兩段遞推最小二乘?偽逆（ rls ? pi ）演算法等。
  11. In this thesis, autoregressive moving average ( arma ) models are applied to identify the flow regimes of two - phase flow. experiments are carried out on the facilities include gas - solid fluidized bed and gas - liquid two - phase flow pipelines. the results show that the methods are effective

    本文基於arma （ autoregressivemovingaverage ）模型，對氣固流化床和氣液兩相流系統進行了分析並進一步對其流型進行了辨識，得到了一些有益的結論。
  12. Autoregressive moving average

    自回歸滑動平均
  13. Immunity mix algorithm based on the continuous differential function is proposed in this paper, and its astringency is proved. from here we get the accurate estimator method of the autoregressive moving average model coefficient

    本文首先提出了通用的基於連續可導函數的免疫混合演算法，並證明了其收斂性，由此我們得到了自回歸滑動均值模型系數的精確估計方法。
  14. By using autoregressive integrated moving average model and on the basis of chinese textile and clothing export data from january of 2000 to december of 2004, this paper carries out forecast analysis for the chinese textile clothing export tendency of 2005 and 2006

    摘要本文利用單整自回歸移動平均模型，依據2000年1月至2004年12月中國紡織品服裝出口額數據，對2005年和2006年中國紡織品服裝出口走勢進行預測分析。
  15. Narma ( nonlinear autoregressive moving average ) neutral network current controller of pmsm is proposed and it in company with neutral network speed regulator composes the pmsm vector control system

    提出永磁同步電動機的narma神經網路電流控制器，並以神經網路速度控制器和narma電流控制器構成電動機矢量控制系統。
  16. Trend prediction and fault diagnosis tech., etc. the information intelligent processing technology facing the application is presented as an emphasis. after introducing the development situation and the whole pattern on related fields, this paper describes several algorithm applied in the simulation experiment, including direct multi - steps nonlinear autoregressive - moving average ( narma ) prediction model based on diagonal recurrent neural networks and fuzzy neural networks model based on generalized probability sum ( gps ) and generalized probability product ( gpp ), and lists the algorithm steps facing the application

    作為重點，本文辟用了較大的篇幅討論面向應用（主要是趨勢預測與故障診斷）的集成智能信息處理技術，在介紹相關領域的發展情況和總體格局之後，重點闡述了幾種基於神經網路的智能演算法，包括基於對角遞歸神經網路（ drnn ）的直接多步非線性自回歸滑動平均（ narma ）預測模型，以及基於廣義概率和（ gps ）與廣義概率積（ gpp ）兩種運算元的模糊神經網路模型，給出了它們的詳細演算法及面向應用的運算步驟。
  17. For the general season time series, according to the model of season autoregressive integrated moving average, the concept of horizontal and lengthways trend are gave, and a new season time series model is brought forward. and then the process of modeling is simplified consumedly. to the estimate problem of a kind of time series, the model performance is good

    對於一般的季節時間序列，我們基於季節自回歸求和均值模型，引入了橫向和縱向趨勢的概念，提出了一類新的季節時間序列模型，大大簡化了建模的過程，對一類時間序列的預測問題，模型性能表現良好。
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