Error Comparison of Sinc Function Interpolation

In MATLAB, higher precision numerical values can be obtained by performing interpolation on the sinc function. We can compare the interpolated results with the original values to determine the magnitude of error. Through this comparison, we can better understand the interpolation process and its impact on results, thereby enabling more effective application of interpolation methods to solve practical problems.

Implementation typically involves using MATLAB's built-in interpolation functions such as interp1 or spline to upsample the original sinc data. The standard approach includes: - Generating the original sinc function using y = sinc(x) where x is a linearly spaced vector - Applying interpolation methods (linear, cubic, spline) with different sampling rates - Calculating error metrics like RMS error and maximum absolute deviation - Visualizing results using plot comparisons and error distribution graphs Key functions involved: sinc(), interp1(), linspace(), abs(), max(), and mean() for statistical error analysis.