查看文档中的第二个示例: http://www.mathworks.com/help/stats/nlinfit.html

Check out the second example in the docs: http://www.mathworks.com/help/stats/nlinfit.html

基本上，您将函数句柄作为您的modelfunction参数传递.要么在文件中创建一个函数，然后在函数名称前传递一个@，要么创建一个匿名函数，如下所示:

Basically you pass a function handle as your modelfunction parameter. Either make a function in a file and then just pass it the function name with an @ in front or else make an anonymous function like this:

nlinfit(x, y, @(b,x)(b(1).*exp(b(2).*x) + b(3)), beta0)

您会注意到，在上文中，我将所有参数都固定在一个向量中.函数的第一个参数必须是要尝试求解的所有点的矢量(例如，在您的情况下为A，B和C)，第二个必须为x.

You'll notice that in the above I have stuck all your parameters into a single vector. The first parameter of your function must be a vector of all the points you are trying to solve for (i.e. A, B and C in your case) and the second must be x.

就像木片所说的那样，beta0是您的起点，因此，您对A，B和C参数的***猜测(不一定是很大的).所以类似[1 1 1]或rand(3,1)的东西，这是非常特定于问题的.您应该玩一些.请记住，这是一个本地搜索功能，因此可能会卡在本地最优值上，因此您的出发点实际上可能非常重要.

As woodchips has said beta0 is your starting point so your best guess (doesn't have to be great) of your A, B and C parameters. so something like [1 1 1] or rand(3,1), it is very problem specific though. You should play around with a few. Just remember that this is a local search function and thus can get stuck on local optima so your starting points can actually be quite important.