ar error 中文意思是什麼
ar error
解釋
二階自回歸誤差-
( 2 ) when analyzing the testing method for the " day - of - week effect ", we considered the ols regression effect and the testing power will decrease because of the different variable in real securities data. we considered the identification. estimation and hypothesis testing of the linear regression model with one rank auto - regression ( ar ( 1 ) ) error ( 3 ) we considered how investors reasonably utilize the testing result of the " day - of - week effect " to direct the investment strategies after obtained it
（ 2 ）在對「周內效應」的檢驗方法進行分析時，考慮到實際證券數據由於具有異方差性，使得利用ols進行回歸將導致回歸效果和檢驗的勢降低，我們對于具有一階自回歸誤差的線性回歸模型，仔細討論了該模型的識別、估計和對相應參數假設檢驗的方法。 -
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。 -
Based on the statere configuration system, an unknown input observer is presented for the prediction of time series. the approximate error of the ar model is regarded as the unknown - input of system
以實時擬合時間序列的線性ar模型作為時變系統的已知線性部分，將擬合誤差作為時變系統的未知輸入，實現了系統狀態的多步預測。 -
The bic method generalized from ar model was adopted to determine the number of input neurons in grnn prediction model. the grnn was applied to single - step and multi - step ahead prediction of the vibration time series of a rotating machine, and its performance was compared with that of 3 - layers perceptrons network with error back propagation training algorithm ( bpnn ). it is indicated that the grnn is more appropriate for prediction of time series than the bpnn, and the performance of grnn is qualified even with sparse sample data
研究了基於廣義回歸神經網路（ grnn ）的大型旋轉機械振動狀態預測，提出了應用bic準則確定grnn預測模型輸入神經元數目的方法，將grnn用於大型機組振動峰?峰值時間序列的預測，與採用誤差反向傳播學習演算法的三層前饋感知器網路（ bpnn ）的預測結果對比表明， grnn的預測性能優于bpnn ，而且，即使樣本數據稀少，也能獲得滿意的預測結果。
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