Question

我的数据在数组中是指数式的，如e ^（ax + c）+ d。我试图为他们画一个合适的东西。

a = data1 (:,1);
b = data1 (:,2);
log(b);
p = polyfit (a,log(b),1);

但我现在不知道该怎么做。我通过polyfit找到了一个方程式，我希望得到我从polyfit得到的方程的指数

exp (0.5632x+2.435)

但我发现它并没有像那样工作。有没有人有任何建议？

Answer 1

尝试使用非线性拟合：

%% PARAMETERS (you need this part)
clear all;
clc, clf;

N        = 128; % number of datapoints
Nint     = N*10; % number of datapoints for curve interpolation
fun      = @(prms,x)  prms(4).^(prms(1)*x+prms(2))+prms(3); % write your function
iniPrm = rand(4,1); % find some initial values for the parameters (choose meaningful values for better results)

%% SIMULATE DATA (this is only for testing purposes)
SNR      = .01; % signal to noise ratio for simulated data 
noise    = (rand(1,N)-.5)*SNR; % create some random noise 
x        = linspace(0,10,N); % create the x axis
y        = fun(iniPrm,x) + noise; % simulate a dataset that follows the given function
x        = x(:); % reshape as a vector
y        = y(:); % reshape as a vector
X        = linspace(x(1),x(end),Nint); % interpolate the output to plot it smoothly
plot(x,y,'.r','markersize',10); hold on; % plot the dataset
%% FIT AND INTERPOLATE YOUR MODEL
[out.BETA,out.RESID,out.J,out.COVB,out.MSE] = nlinfit(x,y,fun,iniPrm,[]); % model your data
[out.YPRED,out.DELTA]  = nlpredci(fun,X,out.BETA,out.RESID,'Covar',out.COVB); % interpolate your model
out.YPREDLOWCI         = out.YPRED - out.DELTA; % find lower confidence intervals of your fitting
out.YPREDUPCI          = out.YPRED + out.DELTA; % find upper confidence intervals of your fitting
out.X                  = X; % store the interpolated X
%% PLOT FITTING
plotCI = @(IO,spec) patch([IO.X(:);flipud(IO.X(:))],[IO.YPREDLOWCI(:);flipud(IO.YPREDUPCI(:))],spec{:}); % create patches: IE: patch(0:10,10:-1:0,ones(10,1)-1,1,{'r','facealpha',0.2})
plot(X,out.YPRED,'-b','linewidth',3);
plotCI(out,{'r','facealpha',.3,'edgealpha',0})

matlab的指数拟合

1 个答案: