面形检测采用边缘定位的方法，并且结合了曲线拟合的方法求解一维位移场来计算误差。
It USES edge localization and curve fitting to solve one dimension displacement field and calculate the error.
结论LMS法简单易实现，拟合的百分位数曲线光滑，拟合误差小。
CONCLUSION LMS is a simple method. Curves fitted by it are much smoother and the fitting errors are much fewer.
采用多项式拟合曲线，可以发挥其便于计算和分析的优点，能够更好地反映电流互感器10%误差特性，使用方便。
The interpolation polynomial is applied in curve fitting for easy calculation and analysis and better representation of CT10%error curve.
描述了确定三次样条曲线拟合参数的方法。参数包括节点数、节点位置和与系统误差相联系的权重。
The parameters including knot Numbers, knot positions and the weights associated with the systematic errors in the cubic spline curve fitting method were discussed.
其中曲线拟合是一个在给定误差下进行曲线逼近的过程，当曲线拟合完成之后，整个曲面的控制点的数目也就确定了。
Curve fitting is a curve approaching process under definite error and the number of control points throughout surface is ascertained after curve fitting completed.
使用该法能获得具有较好的光顺性和一致性的误差拟合曲线。
We could obtain smooth and sole error fitting curve by means of this method.
结果表明采用曲线拟合法能使测距绝对误差在3个采样点以内，与传统的反射波起始点确定方法相比有较大优势。
The absolute error is no more than 3 sampling points, so it has more advantages compared with the traditional methods to judge the start point of reflect wave.
毒性实验存在随机误差，同时剂量-效应曲线（DRC）拟合也会产生误差。
Random error exists not only in toxicity experiments but also in the fitting of dose-response curves(DRC).
提出了一种基于最小均方误差准则的圆弧分段曲线拟合方法。
We present a method of circular arc fragmented curve-fitting based on least mean-square error rule in this paper.
通过实例计算表明，该方法可以高精度地将全区域范围的水泵试验数据拟合成光滑的特性曲线，并能够消除因试验随机误差所引起的曲线扰动。
It is proved in the examples that the new method works well in precisely building the pump test data in wide area into smooth curve without disturbance caused by the random test error.
该测量方法具有测量采集点精度高、齿形轮廓曲线拟合误差小、测量过程与误差处理过程人工干预少、测量精度高等特点。
The method has higher precision of getting measuring points, less fitting error of tooth form, less artificial interpose in the measuring process and higher measuring precision.
图版拟合是现代试井分析的主要方法，由于图版上典型曲线分辨率的限制，使得典型曲线图版拟合分析不可避免地存在误差。
Type curve match analysis is the main method of modern well test interpretation, but the error of this method is unavoidable because of the limitation of the type curves in a sample plot.
与传统的曲线拟合方法相比，该方法只需给定数据点及允许误差即可得到匹配的曲线方程。
Conparing with the conventional curve fitting, this method is simpler, the matched curve could be obtained by only giving the data points and the acceptable error.
本文提出一种新的基于三次样条曲线的面积误差反馈曲线拟合方法。
A new curve fitting method using area error feedback based on cubic splines is presented.
对离散误差资料进行曲线拟合，误差曲线质量对补偿器性能至关重要。
The compensator function depends directly on the quality of the error curve, which is decided by discrete error data through curve fitting.
通过对测量数据的噪声点过滤，数据精简等预处理，采用NURBS曲线拟合螺杆型线并分析了其拟合误差。
The NURBS curve is used for fitting the screw profile when cloud datas pretreatment is already done such as eliminated noise data and data reduction, the fitting error is also analyzed.
结果表明，方法误差与随机噪声的性质有关：在本底噪声为主时，二次曲线拟合法精度高，计算量小。
The result shows that the parabolic fit method has a high precision with fewer computing time when the background noise is the main noise source.
分析了双圆弧拟合的原理，并对逼近误差进行分析，满足了加工拟合曲线的要求。
The principle of bi-arc segment approximating mode is analyzed. According to the analysis of the error, the manufacture requirement is satisfied with the analyzed arc interpolating curves.
并提出了二次曲线拟合的误差校正方法。
Two order polynomial fit method to calibrate error is put forward.
基于实测数据，采用曲线拟合的方法，建立容栅传感器误差的数学模型，确定其变化规律。
The Mathematical model of errors of capacitive gate transducers is established and the changing rule determined in accordance with measured data, and the curve fitting method.
采用响应面法建立了表示比吸能随材料参数而变化的曲面，和高次多项式拟合时的相对误差曲线。
Then the response surface method(RSM) is utilized to establish the response surfaces or curve of SEA vs. material parameters and the curves of relative error in high-order polynomial fitting.
测量误差的主要来源是样品校正曲线拟合误差、样品测定重复性标准偏差。
The main sources of uncertainty come from non-linear calibration models and pre-processing step of samples.
测量误差的主要来源是样品校正曲线拟合误差、样品测定重复性标准偏差。
The main sources of uncertainty come from non-linear calibration models and pre-processing step of samples.
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