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时序残差
相关语句
  time series error
     A Verhulst Model on Time Series Error Corrected for Port Throughput Forecasting
     港口吞吐量预测的时序残差修正Verhulst模型
短句来源
     First of all, in the paper, the author introduces the basic theories and models, and especially does the systemic research on time series models, grey models, regression models and RBF neural network model, and moreover brings the grey Markov-chain residual modification model, the grey Fourier residual modification model, the grey model on time series error corrected, GOM model and grey nonlinear model to the port container throughput forecasting for the first time.
     本研究着重分析了时间系列模型、灰色系列模型、回归模型和RBF神经网络模型四大类预测模型的特点,将灰色马尔科夫链模型、灰色傅利叶模型、时序残差修正模型、灰色优化模型和灰色非线性模型引入到港口集装箱吞吐量预测中。
短句来源
     According to the increment law of port throughput and through typical examples, we propose the Verhulst model on time series error for middle & long-term throughput forecasting.
     根据港口吞吐量的增长规律,通过典型实例提出了基于时序残差的港口吞吐量预测Verhulst模型,用于中长期港口吞吐量预测。
短句来源
  time series error corrected
     A Verhulst Model on Time Series Error Corrected for Port Throughput Forecasting
     港口吞吐量预测的时序残差修正Verhulst模型
短句来源
     First of all, in the paper, the author introduces the basic theories and models, and especially does the systemic research on time series models, grey models, regression models and RBF neural network model, and moreover brings the grey Markov-chain residual modification model, the grey Fourier residual modification model, the grey model on time series error corrected, GOM model and grey nonlinear model to the port container throughput forecasting for the first time.
     本研究着重分析了时间系列模型、灰色系列模型、回归模型和RBF神经网络模型四大类预测模型的特点,将灰色马尔科夫链模型、灰色傅利叶模型、时序残差修正模型、灰色优化模型和灰色非线性模型引入到港口集装箱吞吐量预测中。
短句来源
  time sequence error
     GM(1,1) Model of Time Sequence Error
     时序残差GM(1,1)模型
短句来源
     2. putting forward H(0)tt-GM(1,1) model of time sequence error according to the influence of x(0)-GM(1,1) model precision by time sequence error, H(0)tt-GM(1,1) and x(0)-GM(1,1) models are used to test model precision and forecast.
     2.估计时序残差对灰色x-GM(1,1)模型精度的影响,提出时序残差H-GM(1,1)模型,并利用时序残差H-GM(1,1)模型和x-GM(1,1)模型联合进行精度检验和预测,能较好地提高模型精度和预测精度。
短句来源
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ε (0) t GM(1,1) model of time sequence error has been put forward according to the influence of x (0) GM(1,1) model precision by time sequence error, ε (0) t GM(1,1) and x (0) GM(1,1) models are used to test model precision and forecast. An example is employed to show that the precision of model and prediction could be raised effectively.

顾及时序残差对灰色x(0)-GM(1,1)模型精度的影响,提出时序残差ε(0)t-GM(1,1)模型,并利用时序残差ε(0)t-GM(1,1)模型和x(0)-GM(1,1)模型联合进行精度检验和预测,能较好地提高模型精度和预测精度,实例说明是有效的。

The grey system model is efficient for long-term port throughput forecasting. However, it is imperfect when the throughput increases in the curve with S type or the increment of throughput is in the saturation stage. In this case, the throughput forecasting error of grey system model will become larger and the result is unaccepted. According to the increment law of port throughput and through typical examples, we propose the Verhulst model on time series error for middle & long-term throughput forecasting. The...

The grey system model is efficient for long-term port throughput forecasting. However, it is imperfect when the throughput increases in the curve with S type or the increment of throughput is in the saturation stage. In this case, the throughput forecasting error of grey system model will become larger and the result is unaccepted. According to the increment law of port throughput and through typical examples, we propose the Verhulst model on time series error for middle & long-term throughput forecasting. The result shows that when the throughput increases according to the curve with S type, not only higher forecasting accuracy can be obtained, but also the superiority and features of grey system model can be reserved.

灰色系统预测模型是一种进行港口吞吐量预测的有效方法。但是,当港口吞吐量按照“S”型曲线增长或增长处于饱和阶段时,采用灰色模型进行吞吐量预测的误差较大,预测精度不能满足实际要求。根据港口吞吐量的增长规律,通过典型实例提出了基于时序残差的港口吞吐量预测Verhulst模型,用于中长期港口吞吐量预测。应用结果表明,本模型对于那些暂时处于快速增长而从长远看按“S”型曲线增长的港口吞吐量预测具有较高的预测精度,同时保留了灰色预测方法的原有优势和特点。

 
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