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- The paper introduces the grey forecasting model and analy ses its prediction error as well as application in detail. 詳細介紹了灰色預測方法并分析了預測誤差及其實(shí)用價(jià)值。
- Based on the method of minimum prediction error control, a multiple model adaptive controller( MMAC) for discrete time is presented. 基于最小預測誤差控制器設計方法,設計離散時(shí)間系統多模型自適應控制器,并引入“局部化”方法。
- Through comparing their sums of squared error,it was concluded that prediction error algorithm-based OE model has the best precision. 通過(guò)誤差平方和的比較,確定利用基于輸出誤差(OE)模型的預報誤差法所建立的模型的精度最高。
- Based on the method of minimum prediction error control, a multiple model adaptive controller (MMAC) for discrete time is presented. 基于最小預測誤差控制器設計方法,設計離散時(shí)間系統多模型自適應控制器,并引入“局部化”方法。
- Then the NN model is trained and the average prediction error is 26.46%, which reaches the demand of environmental management. 經(jīng)過(guò)網(wǎng)絡(luò )訓練，預測平均誤差為26.;46%25，滿(mǎn)足環(huán)境管理的精度要求。
- The prediction error percentage of the 52000t all-purpose cargo ship compared with the model test result is 21.8%, 300000t VLCC compared with the measured value is merely 3.64%. 用該公式預報的5.;2萬(wàn)噸多用途貨船的脈動(dòng)壓力值與空泡水筒中的實(shí)驗值相比誤差為21
- A recursive prediction error algorithm which converges fast is applied to tra. 采用了收斂速度較快的遞推預報誤差算法訓練神經(jīng)網(wǎng)絡(luò )。
- Wavelet networks are used to model the prediction error to compensate for the predictive output. 為了提高輸出預報精度，采用小波網(wǎng)絡(luò )對預報誤差進(jìn)行預測，作為輸出預報的補償。
- On the basis of hypothesis of first ordered linear error this paper derived the kinematics posture error model of end operator. 摘要基于一階線(xiàn)性誤差假設推導了空間機器人末端操作器運動(dòng)學(xué)位姿誤差模型。
- Meanwhile, GM (1,1) is of higher precision and less prediction errors than Metabolizing Model's. 而在此實(shí)例中,GM(1,1)模型比新陳代謝的預測精度更高、預測誤差更小。
- Objective To introduce the basic theory of linear measurement error model and apply it to medical research. 目的介紹線(xiàn)性測量誤差模型的基本理論,探討它在醫學(xué)研究領(lǐng)域中的應用。
- An MD prediction error coding method is also proposed using low quality macroblock update. 該方案在丟包環(huán)境下取得較好的抗丟包性能。
- SINS error model and the Kalman filter are used for realizing precise alignment of the SINS under vibration state. 仿真結果表明，補償擺動(dòng)誤差后，基本消除車(chē)體擺動(dòng)干擾，得到與靜基座粗對準相當的結果；
- It is proved to have the same asymptotic statistical properties as the prediction error method(PEM). 證明了該方法與預報誤差法具有相同的漸近和統計性能。
- The error model of system is set up, the precision and velocity of the aligning scheme is further researched. 建立了系統的誤差模型，深入研究了該對準方案的精度和速度，并與傳統的卡爾曼濾波器進(jìn)行仿真比較。
- In the method, the criterion of final prediction error (FPE) is employed to determine the embedding dimension of samples. 該方法應用最終預報誤差(FinalPrediction Error,FPE)準則確定樣本的嵌入維數。
- Example verified that linear measurement error model can fit data with great measurement error and can give fine results. 線(xiàn)性測量誤差模型能夠在很大程度上解決由測量誤差帶來(lái)的問(wèn)題,針對具體問(wèn)題擬合恰當的線(xiàn)性測量誤差模型,可以得到合理的結果。
- The algorithm prediction error is larger under ionospheric stormy conditions, which is more prominent for the stations in the low latitudes. 此次磁暴期間，算法的精度明顯降低，對于低緯地區的影響更為顯著(zhù)；
- The empirical results show that Fractionally integrated GARCH with GED error model performs the best in estimating five percent VaR. 實(shí)證結果表明在估計95%25置信度下的VaR值時(shí)基于GED分布的FIGARCH(1,d,1)模型表現最佳。
- The result shows that a second order ARMA model gives the best fit to the data in terms of sum of squares of prediction errors and in terms of the whiteness of the residual. 兩種算法都進(jìn)行了仿真，結果表明：從預測偏差的平方和及殘數的空白度來(lái)看，二階的ARMA模型會(huì )產(chǎn)生最佳的數據適應度。
