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.
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.
在第二章，我们分别用二个滑动平均模型去模拟盈余过程。在常值利率的作用下我们来研究该类模型的破产概率。
In Chapter 2, two moving average risk models are introduced to model the surplus process, and the probabilities of ruin are examined in models with a constant interest force.
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.
接下来就是进行过程监控方法的研究。本文主要对生产过程利用可适应性指数加权滑动平均(aewma)法进行质量控制。
Then we use adaptive exponentially weighted moving average (AEWMA) control charts to control the process which satisfies the two-component model.
接下来就是进行过程监控方法的研究。本文主要对生产过程利用可适应性指数加权滑动平均(aewma)法进行质量控制。
Then we use adaptive exponentially weighted moving average (AEWMA) control charts to control the process which satisfies the two-component model.
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