Identification of Black Plastics Based on Fuzzy RBF Neural Networks: Focused on Data Preprocessing Techniques Through Fourier Transform Infrared Radiation

The performance enhancement of system identification of various plastic materials to effectively recycle the waste plastics arises as a key issue studied here. For black plastics, which contain carbon black, one is unable to discriminate it from other materials. To facilitate the identification process, Fourier transform-infrared with attenuated total reflectance is used to carry out qualitative as well as quantitative analysis of black plastics. Since a spectrum obtained in this manner constitutes highly dimensional data, feature reduction becomes necessary to extract sound features and reduce the dimensionality of the original spectrum. In this study, three types of feature extraction techniques are considered: peak detection technique, feature extraction based on the chemical characteristics, and fuzzy transform-based feature extraction to determine sound discriminative features. In order to enhance classification process, fuzzy radial basis function neural networks classifier is constructed; these architectures of the classifiers take advantage of the hybrid technologies. Based upon experimental studies, it is shown that the proposed classification system with the feature extraction techniques exhibits superior performance over the performance reported for the already studied classifiers.