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. This chapter discusses an interpolation-based numerical integration method, and introduces spline function-based numerical differentiations and integrations. The chapter explains the correlation analysis of signals and experimental data. Many interpolation functions have been provided in MATLAB, such as the one-dimensional interpolation function, two-dimensional and the polynomial fitting function. The Spline Toolbox provided in MATLAB can be used to better solve the interpolation problems. Spline functions can be regarded as an effective approximation method. The most widely used spline functions are the cubic splines and B-splines. The cubic and B-splines can be used to approximate the integrand that is defined by a given data set. Mittag–Leffler function is the extension of simple exponential functions.
