一个x对应两组y的数据在matlab中拟合怎样将曲线显示到一个图形中t=[79.55 89.55 99.55 109.55 119.55 129.55 139.55 149.55 159.55 169.55 179.55 189.55 199.55 209.55 219.55 229.55 239.55 249.55 259.55 269.55 279.55 289.55 299.55 309

一个x对应两组y的数据在matlab中拟合怎样将曲线显示到一个图形中
t=[79.55 89.55 99.55 109.55 119.55 129.55 139.55 149.55 159.55 169.55 179.55 189.55 199.55 209.55 219.55 229.55 239.55 249.55 259.55 269.55 279.55 289.55 299.55 309.55 319.55 329.55 339.55 349.55 359.55 369.55 379.55 389.55];
c=[0.53101 0.52179 0.50123 0.47652 0.45231 0.42542 0.40997 0.38728 0.37084 0.35515 0.33933 0.32311 0.31735 0.30787 0.3017 0.2972 0.28969 0.28652 0.28727 0.28915 0.29334 0.29975 0.30972 0.32113 0.33944 0.35925 0.37153 0.39057 0.45678 0.48005 0.47624 0.50166];
d=[0.51879 0.50849 0.48348 0.46559 0.4435 0.4135 0.40336 0.382 0.36632 0.35128 0.33528 0.32028 0.3155 0.30666 0.30121 0.2964 0.29102 0.28939 0.28983 0.2934 0.30028 0.30755 0.31798 0.32893 0.34736 0.36269 0.37796 0.3948 0.4401 0.46196 0.48057 0.50569];
t是自变量，c和d分别是两次测量的数据，要两次数据都和t拟合，而且要两个拟合曲线显示在一起，以形成对比，求程序，或者指导，

t=[79.55 89.55 99.55 109.55 119.55 129.55 139.55 149.55 159.55 169.55 179.55 189.55 199.55 209.55 219.55 229.55 239.55 249.55 259.55 269.55 279.55 289.55 299.55 309.55 319.55 329.55 339.55 349.55 359.55 369.55 379.55 389.55];
c=[0.53101 0.52179 0.50123 0.47652 0.45231 0.42542 0.40997 0.38728 0.37084 0.35515 0.33933 0.32311 0.31735 0.30787 0.3017 0.2972 0.28969 0.28652 0.28727 0.28915 0.29334 0.29975 0.30972 0.32113 0.33944 0.35925 0.37153 0.39057 0.45678 0.48005 0.47624 0.50166];
d=[0.51879 0.50849 0.48348 0.46559 0.4435 0.4135 0.40336 0.382 0.36632 0.35128 0.33528 0.32028 0.3155 0.30666 0.30121 0.2964 0.29102 0.28939 0.28983 0.2934 0.30028 0.30755 0.31798 0.32893 0.34736 0.36269 0.37796 0.3948 0.4401 0.46196 0.48057 0.50569];
p1=polyfit(t,c,2);
p2=polyfit(t,d,2);
plot(t,c,'r',t,polyval(p1,t),'r',t,d,'b',t,polyval(p2,t),'b','linewidth',2)
红色为一组,蓝色为一组
祝你学习愉快!

（1）通过对原始数据绘图可看出t-c、t-d都基本符合二次曲线，所以就用二次多项式来拟合所给数据；

（2）代码如下，为了看效果，分别画在两幅图中：

t=[79.55 89.55 99.55 109.55 119.55 129.55 139.55 149.55 159.55 169.55 179.55 189.55 199.55 209.55 219.55 229.55 239.55 249.55 259.55 269.55 279.55 289.55 299.55 309.55 319.55 329.55 339.55 349.55 359.55 369.55 379.55 389.55];

c=[0.53101 0.52179 0.50123 0.47652 0.45231 0.42542 0.40997 0.38728 0.37084 0.35515 0.33933 0.32311 0.31735 0.30787 0.3017 0.2972 0.28969 0.28652 0.28727 0.28915 0.29334 0.29975 0.30972 0.32113 0.33944 0.35925 0.37153 0.39057 0.45678 0.48005 0.47624 0.50166];

d=[0.51879 0.50849 0.48348 0.46559 0.4435 0.4135 0.40336 0.382 0.36632 0.35128 0.33528 0.32028 0.3155 0.30666 0.30121 0.2964 0.29102 0.28939 0.28983 0.2934 0.30028 0.30755 0.31798 0.32893 0.34736 0.36269 0.37796 0.3948 0.4401 0.46196 0.48057 0.50569];

p1 = polyfit(t,c,2); %用来拟合t-c的二次多项式，多项式的系数按降幂排列在数组p1中

p2 = polyfit(t,d,2); %用来拟合t-d的二次多项式，多项式的系数按降幂排列在数组p2中

fc = polyval(p1,t); %计算拟合值

fd = polyval(p2,t); %计算拟合值

%plot(t,c,t,fc,t,d,t,fd,'linewidth',2)

subplot(1,2,1)

plot(t,c,t,fc,'linewidth',2)

legend('t-c','t-fc')

subplot(1,2,2)

plot(t,d,t,fd,'linewidth',2)

legend('t-d','t-fd')

图如下：