Fuzzy classification algorithms for analysis of polymer spectra

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. 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 special rule based approach with fuzzy if-then rules, a fuzzy c-means algorithm and a fuzzy Kohonen cluster algorithm. The three classification algorithms are compared and results are given