Application of Fast Fourier Transform

Abstract

Fourier analysis is most frequently used as a univariate approach for either modeling or simplifying data. It may also be used as a method for multivariate data analysis. There are various connections between Fourier analysis and trend analysis. It takes a fresh look at how data sets are related. In the case of Fourier analysis, the technique clarifies the time dimension variable in the data set. The most fundamental kind of Fourier analysis works under the idea that many events have a periodic nature and that fluctuations in other variables brought on by this periodicity may be eliminated using Fourier transforms. By using the residual (i.e., time-independent) variance from other variables, Fourier-transformed data may be subjected to more powerful analysis.Based on differential matrices and semidiscrete Fourier transforms, this paper summarizes the key problems in Fourier analysis, FFT. Secondly, this paper points out the application of F FT in the field of modern science and technology and the main progress of current FFT research, and on this basis, the research prospects of FFT law are prospected.