nsqnonlin - 不正确的收敛 - 拟合曲线根本不对我的数据建模
nsqnonlin - Incorrect convergence - Fitted curve does not model my data at all
下图中，红叉为数据，蓝线为拟合曲线。
Data and fit curve - image
这是适合的函数：
function diff = fit_simp(x,X,Y)
% This function is called by lsqnonlin.
% x is a vector which contains the coefficients of the
% equation. X and Y are the option data sets that were
% passed to lsqnonlin.
A1 = x(1);
A2 = x(2);
mode1 = x(3);
mode2 = x(4);
sig1 = x(5);
sig2 = x(6);
alpha = x(7);
beta = x(8);
gamma = x(9);
diff = (A1/(sqrt(2*pi)*sig1))*exp(-0.5.*((X-mode1)/sig1).^2) + (A2/(sqrt(2*pi)*sig2))*exp(-0.5.*((X-mode2)/sig2).^2) + alpha + beta*X + gamma*X.*X
我的起始值是：
X0=[0.1 0.02 0.6 1.2 1 1 0.1 -0.2 0.011]
来自 MATLAB 的最终消息：
Optimization completed: The first-order optimality measure, 3.114006e-10,
is less than options.OptimalityTolerance = 1.000000e-06.
Optimization Metric Options
relative first-order optimality = 3.11e-10 OptimalityTolerance = 1e-06 (default)
有什么我想念的吗？它适用于更简单的功能。
您没有使用您的 Y 数据。现在，您的 diff 是模型在 X 点对参数 x 的评估，但您应该 return diff - Y。 lsqnonlin 将最小化 returned 值的范数。这确实是你得到的，一个到处（几乎）为零的函数。
下图中，红叉为数据，蓝线为拟合曲线。
Data and fit curve - image
这是适合的函数：
function diff = fit_simp(x,X,Y)
% This function is called by lsqnonlin.
% x is a vector which contains the coefficients of the
% equation. X and Y are the option data sets that were
% passed to lsqnonlin.
A1 = x(1);
A2 = x(2);
mode1 = x(3);
mode2 = x(4);
sig1 = x(5);
sig2 = x(6);
alpha = x(7);
beta = x(8);
gamma = x(9);
diff = (A1/(sqrt(2*pi)*sig1))*exp(-0.5.*((X-mode1)/sig1).^2) + (A2/(sqrt(2*pi)*sig2))*exp(-0.5.*((X-mode2)/sig2).^2) + alpha + beta*X + gamma*X.*X
我的起始值是：
X0=[0.1 0.02 0.6 1.2 1 1 0.1 -0.2 0.011]
来自 MATLAB 的最终消息：
Optimization completed: The first-order optimality measure, 3.114006e-10,
is less than options.OptimalityTolerance = 1.000000e-06.
Optimization Metric Options
relative first-order optimality = 3.11e-10 OptimalityTolerance = 1e-06 (default)
有什么我想念的吗？它适用于更简单的功能。
您没有使用您的 Y 数据。现在，您的 diff 是模型在 X 点对参数 x 的评估，但您应该 return diff - Y。 lsqnonlin 将最小化 returned 值的范数。这确实是你得到的，一个到处（几乎）为零的函数。