本文采用自回归滑动平均模型(ARMA)对电力负荷进行了预测。
In this paper, autoregressive moving average model (ARMA) is used to forecast the power load.
提出了一类新的用于非线性时间序列建模的混合自回归滑动平均模型。
We obtain some results as follows:In chapter 2, a new mixture autoregressive moving average model is proposed for modeling nonlinear time series.
提出了一类用于非线性时间序列建模的混合自回归滑动平均模型(MARMA)。
A mixed autoregressive moving average (MARMA) model is proposed for modeling nonlinear time series.
其中内部动态元分别由带有局部激活反馈和局部输出反馈的自回归滑动平均滤波器构成。
The internal dynamic elements are auto-regressive moving average filters with local activation feedback and local output feedback, respectively.
本文提出设计可控自回归滑动平均过程(CARMA)的离散时间模型参考自适应控制新方法。
A new method is suggested in this paper for design discrete-time Model Reference Adoptive control (MRAc) for controlled Auto-regressive Moving Average (CARMA) processes.
根据带控制变量的非线性自回归滑动平均(NARMAX)模型理论，建立了锚泊线的系统模型。
A system model of a mooring line was constructed according to the NARMAX model theory.
基于自回归滑动平均模型(ARMA)的方法，实现了不同车速和路面条件下的随机振动信号的实验室再现。
The reconstruction of vehicle random vibration signal under different vehicle speed and road conditions is realized in laboratory, based on an auto regressive moving average (ARMA) time series model.
基于自回归滑动平均模型（ARMA），利用时间序列建模，提出了利用组合模型对网络流量进行预测的方法。
From the discrete solution of the equation of vibration of engineering structure, the equalility of the neural network based time domain identification and the ARMA model was verified.
本文利用时间序列谱分析和卡尔曼滤波的方法讨论了两个随机过程，主要是自回归滑动平均(ARMA)过程，的叠加问题。
Using the methods of time series spectral analysis and Kalman filter, this article discussed the additive problems of two stochastic processes, mainly Auto Regression Moving Average (ARMA) processes.
数值模拟结果和实际地震数据处理结果表明：自回归滑动平均（ARMA）模型比滑动平均（MA）模型具有参数节省、模型更为高效的特点；
The results of numeric simulation and real seismic data processing showed that the ARMA model was characterized by parameter-economic and high efficiency in comparison with MA model;
该文针对风速随机变化的特性，在风速统计特性研究的基础上，用自回归滑动平均(ARMA)方法建立了具有一定功率谱密度特性的风速模型。
This paper deals with stochastic characteristic of the wind speed, and gives an auto-regressive moving-average (ARMA) model for wind speed subjected to particular power spectral density.
针对板带轧机液压agc系统在线故障诊断问题，建立了一种基于非线性自回归滑动平均模型NARMA的递归神经网络，通过AIC定阶法确定模型阶次。
For on-line fault diagnosis of hydraulic AGC system on strip rolling mill, a recursive neural network model based on NARMA was established. The model order is determined by AIC method.
针对生物发酵过程中温度控制难以建模的问题，基于非线性自回归滑动平均（NARMA）模型，设计了神经网络自回归滑动平均（NN-NARMA）模型。
To solve the problem of modeling temperature control in the fermentation process, a neural network nonlinear auto regressive moving average(NN-NARMA) modeling method for nonlinear system is proposed.
其特点是显著性高、计算量小，并且不受噪声模型自回归阶次等于滑动平均阶次条件的限制。
This algorithm is characterized by its high significance and less computation, being not restricted to the condition that the autoregressive order should be equal to the moving average order.
其特点是显著性高、计算量小，并且不受噪声模型自回归阶次等于滑动平均阶次条件的限制。
This algorithm is characterized by its high significance and less computation, being not restricted to the condition that the autoregressive order should be equal to the moving average order.
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