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fitcurve
- 二乘法曲线拟合 //X,Y -- X,Y两轴的坐标 //M -- 结果变量组数 //N -- 采样数目 //A -- 结果参数 -Using two multiplication fit the curves//X,Y the site of two axial x,y//M the number of outcome variable//N the number of samples//A the parameter of outcome
fit_maxwell_pdf
- fit_maxwell_pdf - Non Linear Least Squares fit of the maxwellian distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples. fits data to the probability of the form:
fit_ML_laplace
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)
fit_ML_log_normal
- fit_ML_normal - Maximum Likelihood fit of the laplace distribution of i.i.d. samples!. Given the samples of a laplace distribution, the PDF parameter is found fits data to the probability of the form: p(x) = 1/(2*b)*exp(-abs(x-u)/b)
fit_ML_maxwell
- fit_ML_normal - Maximum Likelihood fit of the log-normal distribution of i.i.d. samples!. Given the samples of a log-normal distribution, the PDF parameter is found fits data to the probability of the form: p(x) = sqrt(1/(2*pi))/(s*x)*
fit_ML_normal
- fit_ML_normal - Maximum Likelihood fit of the normal distribution of i.i.d. samples!. Given the samples of a normal distribution, the PDF parameter is found fits data to the probability of the form: p(r) = sqrt(1/2/pi/sig^2)*exp(-((r-u
fit_ML_rayleigh
- fit_ML_rayleigh - Maximum Likelihood fit of the rayleigh distribution of i.i.d. samples!. Given the samples of a rayleigh distribution, the PDF parameter is found fits data to the probability of the form: p(r)=r*exp(-r^2/(2*s))/s wit
fit_rayleigh_pdf
- fit_rayleigh_pdf - Non Linear Least Squares fit of the Rayleigh distribution. given the samples of the histogram of the samples, finds the distribution parameter that fits the histogram samples.fits data to the probability of the form: p(r)=r*exp(-
Body-Area-Networks
- 一个身体部位比较模型的新方法网络（BAN）的，可以容纳多个环节和多个科目。所述的绝对测量允许跨频谱可能刻画的比较 从单参数为整个合奏，通过基于参数化到每个活动，每个学科和每个环节模型。使用错误，并明确之间权衡复杂性，在一个善良的适应措施相结合，显示有重要的影响时，适用于一系列典型的禁止通道数据。它是有不同的 在模式的选择的影响，以及它相关的复杂性，混合活动的“日常”的数据，设置活动相比，动态数据（例如步行）。平均路径损耗的不足，甚至位数的路径损失的措施，作为唯一的表征还强调“禁止通道。
curvemain_kcsjjj
- 最小二乘法拟合一个非线性函数（这里是齿轮四杆机构的各边及齿轮大小的拟合） 多变量 多参数 函数表达式复杂（但必须有表达式，只有微分方程不行） 在数据较少的时候 拟合多个参数-A nonlinear function of the least squares fit (in this case each side of the four agencies gear and gear size fitting) complex expression of multi-variable multi-p
FDTD_1D_HzEy
- 一维成像FDTD,TM电磁波各处场强的数值，完全匹配成PML内参数的设置。-A d imaging FDTD, TM electromagnetic wave field numerical everywhere, fit into PML parameter set in
FDTD_2D_TE
- 二维成像FDTD,TE电磁波各处场强的数值，完全匹配成PML内参数的设置-2 d imaging FDTD, TE electromagnetic wave field numerical everywhere, fit into PML parameter set in
Save_File_Data
- 功能是：实现二维成像FDTD算法,TE电磁波各处场强的数值，完全匹配成PML内参数的设置-2 d imaging FDTD, TE electromagnetic wave field numerical everywhere, fit into PML parameter set in
ASA
- Adaptive Simulated Annealing (ASA) is a C-language code developed to statistically find the best global fit of a nonlinear constrained non-convex cost-function over a D-dimensional space. This algorithm permits an annealing schedule for "temper
tscodes
- After trying tones of codes for ARIMA model parameter estimation and prediction, I found this code could be the best one that fit my purpose of predicting temperature. I would like to share it to you. Thanks
sinefitness
- Least squares sinusoid fit algorithm described in IEEE Standard for Digitizing Waveform Recorders (IEEE Std 1057): Algorithm for three-parameter and four-parameter least squares fit to sinewave data using matrix operations. The algorithm is (in
NSEDFEllipsoid
- NSEDFEllipsoid是基于NESEDF椭圆拟合方法改进的椭球拟合方法。 函数alpha = NSEDFEllipsoid(X,Y,Z)根据椭球代数距离公式a(1)x^2 + a(2)y^2 + a(3)z^2 + a(4)xy + a(5)xz + a(6)yz + a(7)x + a(8)y + a(9)z + a(10) = 0拟合得到椭球方程的10个系数，其中X、Y、Z是采样点坐标的列向量集合。 -NSEDFEllipsoid fits ellipsoid based on
qei_my58
- 利用最小二乘算法实现对三维平面的拟合，利用贝叶斯原理估计混合logit模型的参数，未来线路预测,分析误差。- Least-squares algorithm to fit a three-dimensional plane, Bayesian parameter estimation principle mixed logit model, Future line prediction, error analysis.
hun_vn86
- 利用贝叶斯原理估计混合logit模型的参数，包括四元数的各种计算,利用最小二乘算法实现对三维平面的拟合。- Bayesian parameter estimation principle mixed logit model, Including quaternion various calculations, Least-squares algorithm to fit a three-dimensional plane.
gvuax
- Prediction Error Method for Parameter Identification - the idea of relaxation, Least-squares algorithm to fit a three-dimensional plane, Weighted acceleration.