Analysis of polymer spectra using wavelet transforms and fuzzy algorithms

We propose an efficient strategy for qualitative analysis of measured polymer spectra based on fast wavelet transforms and fuzzy set theory. First a wavelet transform is applied to the measured data acting as a feature extractor. Thus huge data sets are enormously compressed and only a few typical features (wavelet coefficients) locating the spectra peaks remain for the identification process. Then, a fuzzy classification algorithm separates different spectra into various clusters thus giving a qualitative interpretation of the ingredients due to calculated membership values. For that we implemented a fuzzy c-means algorithm and a fuzzy Kohonen cluster algorithm. The both cluster algorithms are compared and results are given