ABSTRACT
In scientific research and engineering practice, a lot of experimental data are generated. Based on the experimental data, the problems of data interpolation and function fitting may always be encountered. The existing data can be regarded as the samples, the so-called data interpolation is to numerically generate new data points from a discrete set of known samples. In Section 8.1, one-dimensional, two-dimensional or even high-dimensional interpolation problems are solved in MATLAB. An interpolation-based numerical integration method is also introduced. In Section 8.2, two of the most widely used splines for interpolation are introduced, the cubic spline and the Bspline. Spline function-based numerical differentiations and integrations are also introduced. The integration results are even more accurate than those presented in Chapter 3 and Section 8.1. Data interpolation problems can easily be solved by following the examples in these two sections.
