x = 0:9;
y = [0.053 1.477 1.970 3.279 4.153 4.934 5.178 5.828 6.082 6.484]; % data
X = linspace(0,8.5,1);
Y = interp1(x,y,X,'pchip')
v = [0.053 1.477 1.970 3.279 4.153 4.934 5.178 5.828 6.082 6.484];
xq = 0.5:8.5;
vq = interp1(v,xq);ineedhelpnow said:
ineedhelpnow said:
Linear interpolation in MATLAB is a method of estimating unknown data points in a set of data by drawing a straight line between two known data points and finding the value of the unknown point on that line.
Linear interpolation differs from other methods, such as polynomial or spline interpolation, in that it assumes a linear relationship between data points. This means that the estimated values may not be as accurate for highly nonlinear data sets.
To perform linear interpolation in MATLAB, you can use the "interp1" function. This function takes in a set of known data points and an array of x-values for the unknown points. It then returns an array of estimated y-values for those x-values using linear interpolation.
Yes, linear interpolation can be used to estimate missing values in a data set. However, it is important to note that the accuracy of these estimates may be affected by the spacing of the known data points and the linearity of the data.
One limitation of linear interpolation in MATLAB is that it assumes a linear relationship between data points, which may not always be the case. Additionally, it may not accurately estimate values for data sets with large gaps between known points or for highly nonlinear data.