How to compute the standard error for coefficients returned from fit function?

I fit my data to an exponential model using the FIT function:

 % X data
 for i=1:32
    TE(i)=i*12.4e-3;
 end
 X=TE(2:end);
% Y data
 MeanA=[1.2022e+03, 1.1752e+03, 969.7900, 851.6100, 708.6800, 621.5000, 515.5300, 454.1000, 362.2100, 334.1400, 269.0400, 236.8100, 216.3000, 201.4900, 163.6800, 154.6000, 156.5600, 140.9800, 143.9400, 157.7100, 115.1300, 114.9200, 115.6300, 111.0800, 104.1600, 105.5900, 110.0500, 112.1600, 127.5700, 107.1800, 111.8200, 95.7400];
 Y=MeanA(2:end);

 fo = fitoptions('Method','NonlinearLeastSquares',...
    'Lower',[500,0,-100],...
    'Upper',[9000,30,400],...
    'StartPoint',[1528,15, 90]);
ft = fittype('A*exp(-R2*x)+B','options',fo,'coefficients',{'A','R2','B'});
f2= fit(X',Y',ft)

The result I get from the fit is:

 f1 = 
     General model:
     f2(x) = A*exp(-R2*x)+B
     Coefficients (with 95% confidence bounds):
       A =        1597  (1551, 1643)
       R2 =       15.64  (15.01, 16.27)
       B =       97.78  (89.36, 106.2)

But I don't know how to calculate the standard error of the coefficients (A, B and R2) from the confidence intervals? I know that I can find the standard error of the coefficients using other fit functions such as fitnlm but I need to find the standard error of the coefficients using the "fit" function because I need to apply Lower and Upper bounds.

Thanks very much!