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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
curlfit
- 工程上经常需要对两个或多个非线性参量进行拟合，本压缩包里有各种使用拟合代码，很实用。-Projects often require two or more non-linear parameter fitting with the use of this compression bag fit a variety of code, and very practical.
Sams.LINQ.Unleashed.for.C.Sharp.Jul.2008
- The following typographic conventions are used in this book: Code lines, commands, statements, variables, and text you see onscreen appear in a monospace typeface. Occasionally in listings bold is used to draw attention to the snippet of code b
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(-
nakagamai
- The primary justification for the use of the Nakagami- m fading model is its good fit to empirical fading data. A procedure for simulating both the amplitude and the phase processes of a Nakagami-m fading channel, for arbitrary values of the fading p
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。
AssignmentP6
- 1. For the VHDL model given below (Code List One), compare the FIFOs implementations on CPLD and FPGA. (1) Synthesize and verify (simulate) the VHDL design of the FIFOs (2) For CPLD implementation (fit) of the FIFOs, how many MCs (macrocells)
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
wzrh
- (1)针对在线计算量大这一缺陷，将预测控制中的柔化输出信号的思想推广到柔化输入信号，使得约束条件被简化为仅对当前控制量的约束，可以直接计算得出；同时该方法避免了求逆矩阵，大大减小了计算量，并能够保证控制算法的可行性和良好的控制性能。 (2)针对传统算法中设计参数整定困难这一缺点，应用基于BP神经网络变参数设计的广义预测控制算法，实现了对控制量柔化参数的在线调整。 (3)利用带有遗忘因子的最小二乘法对系统辨识。本文通过仿真发现该方法对于Hénon混沌系统并不完全适用，可考虑利用其他优化系统
SSRN-id2460551
- 关于backtest过拟合的处理方法，描述如何根据参数拟合的次数调整sharp值。-For a backtest overfitting, describe how to adjust the sharp value based on the number of parameter fit times.