根据最小二乘原理和回归模型设计出消除噪声的自适应滤波器，以消除图像的噪声。
An adaptive filter is designed in the paper to remove the noise of an image in terms of the principle of least squares and regression model.
文章讨论如何根据最小二乘原理和回归模型设计出自适应滤波器，以用于消除图像噪声。
This paper discusses how to design an adaptive filter to remove the noise of an image in terms of the principle of least squares and regression model.
对统计算法中回归模型中的假设条件、平滑指数的自适应调整、ARMA模型的参数估计作了一些分析。
We make analyses of the three hypotheses of regression. Adaptive adjustment of smoothing index parameters and parameter estimation of ARMA models.
通过研究网络流量异常检测，提出一种新的基于自适应自回归(aar)模型的在线故障检测算法。
A novel online fault detection algorithm based on adaptive auto-regressive (AAR) model is proposed focusing on the anomaly detection of network traffic.
文中介绍了一种基于时变自回归模型的归一化参数自适应匹配滤波算法。
In this paper, an algorithm of normalized parametric adaptive matched filter based on time-varying autoregressive model is introduced.
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.
与多元线性回归、模糊回归和自适应模糊神经网络相比，该模型学习精度高且具有较好的泛化能力，能取得较好的预测效果。
Comparing with the models based on multiple statistic analysis, generalized regress-ion neural network or adapted fuzzy neural network model, it shows better learning precision and generalization.
提出一种 自适应 自回归（ADAR）预测模型，可根据谐波过程特性变化 自适应 调整 自回归预测模型的参数乃至结构。
ADAR(ADaptive AR) predicting model is presented, whose parameters and exponent number can be adaptively tuned according to the characteristic variation of harmonic.
提出了一种具有强非线性表达能力的自适应偏最小二乘回归(APLSR)方法，并应用于初顶石脑油干点软测量模型建立。
A novel adapting partial least square regression (APLS r) approach was proposed to develop the naphtha dry point soft sensor of the primary distillation tower.
提出了一种具有强非线性表达能力的自适应偏最小二乘回归(APLSR)方法，并应用于初顶石脑油干点软测量模型建立。
A novel adapting partial least square regression (APLS r) approach was proposed to develop the naphtha dry point soft sensor of the primary distillation tower.
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