Identification of black plastics realized with the aid of Raman spectroscopy and fuzzy radial basis function neural networks classifier

To accomplish the effective classifier and secure the accurate classification capabilities of black plastics, a comprehensive design methodology of fuzzy radial basis function neural networks is developed with the aid of principal component analysis and particle swarm optimization. Plastics recycling is the competitive method which can deal with the shortage of natural resource. To recycle and reuse the waste plastics, this study is given as the key issue to identify and classify waste plastics by resin type such as polyethylene terephthalate, polypropylene, polystyrene, etc. To complement the weak points of recognition and classification of the near-infrared radiation equipment, Raman spectroscopy is used to obtain qualitative as well as quantitative analysis of black plastics. To improve the identification performance of black plastics, an intelligent computing algorithm such as fuzzy radial basis function neural networks classifier and preprocessing algorithm as principal component analysis are applied to analyze and classify the obtained spectrum of black plastics. Finally, to optimize the structure as well as parameters of fuzzy radial basis function neural networks, particle swarm optimization technique is used. The obtained experimental results show that the proposed network architecture exhibits high classification capabilities in practical applications.