Iteration limit exceeded - nlinfit for non-linear regression

Hi, I'm performing a nonlinear regression using nlinfit, and I keep getting the warning "iteration limit exceeded". I need to find 3 unknowns which I get but the same error is displayed each time. The function is y = C*(L)^m*(G)^n. I've also attached the code below for reference.

clearvars; close all; clc;

z = [87.1446607 94.47343938 99.51904391 108.3315307 134.1848125]'; %Ky_a

x = [2.008969 2.012897 1.999 2.008 2.0092]'; %L

y = [1.883907496 2.04141846 2.187417647 2.461654149 2.617206617]'; %G

XY = [x y];

modelfun = @(b,XY)(b(1).*((XY(:,1).^b(2)).*(XY(:,2).^b(3)))); % C*((L^m)*(G^n))

% C, m and n here are b(1), b(2), and b(3)

beta0 = [0.5;0.5;0.5]; % initial guesses for b(1), b(2) and b(3)

%function below finds the value of the unknowns and finds

%residuals, MSE, jacobian matrix and covariance matrix

[beta,R,J,CovB,MSE,ErrorModelInfo] = nlinfit(XY,z,modelfun,beta0);

Answer 1

[Star Strider]

I used fitnlm here instead, since it outputs all the statistics as well as the parameter estimated. (It calls nlinfit so is essentially the same.)

z = [87.1446607 94.47343938 99.51904391 108.3315307 134.1848125]'; %Ky_a
x = [2.008969 2.012897 1.999 2.008 2.0092]'; %L
y = [1.883907496 2.04141846 2.187417647 2.461654149 2.617206617]'; %G
XY = [x y];
modelfun = @(b,XY)(b(1).*((XY(:,1).^b(2)).*(XY(:,2).^b(3))));  % C*((L^m)*(G^n)) 
% C, m and n here are b(1), b(2), and b(3)
beta0 = [0.5;0.5;0.5]; % initial guesses for b(1), b(2) and b(3)
opts = statset('MaxIter',5000);                                         % Tell It To Keep Iterating Until It Converges
mdl = fitnlm(XY,z,modelfun,beta0,'Options',opts)                        % Fit Function, Dispaly Tesults

mdl = 
Nonlinear regression model:
    y ~ (b1*((x1^b2)*(x2^b3)))

Estimated Coefficients:
          Estimate      SE        tStat       pValue 
          ________    _______    ________    ________

    b1    0.25048      2.8795    0.086987     0.93861
    b2     7.2317      16.492     0.43849     0.70385
    b3      1.233     0.29945      4.1174    0.054233


Number of observations: 5, Error degrees of freedom: 2
Root Mean Squared Error: 8.34
R-Squared: 0.895,  Adjusted R-Squared 0.79
F-statistic vs. zero model: 269, p-value = 0.00371

[ze,zeci] = predict(mdl, XY);

figure
stem3(x, y, z, 'filled')
hold on
plot3(x, y, ze)
plot3(x, y, zeci, '--k')
hold off
xlabel('x')
ylabel('y')
zlabel('z')
legend('Data','Function Fit','Confidence Intervals', 'Location','best')

.