| Title |
Design of Fuzzy Clustering-based Neural Networks Classifier for Sorting Black Plastics with the Aid of Raman Spectroscopy |
| Authors |
김은후(Kim, Eun-Hu) ; 배종수(Bae, Jong-Soo) ; 오성권(Oh, Sung-Kwun) |
| DOI |
https://doi.org/10.5370/KIEE.2017.66.7.1131 |
| Keywords |
Raman spectroscopy ; Radial basis function neural networks ; Artificial bee colony ; Principal component analysis |
| Abstract |
This study is concerned with a design methodology of optimized fuzzy clustering-based neural network classifier for classifying black plastic. Since the amount of waste plastic is increased every year, the technique for recycling waste plastic is getting more attention. The proposed classifier is on a basis of architecture of radial basis function neural network. The hidden layer of the proposed classifier is composed to FCM clustering instead of activation functions, while connection weights are formed as the linear functions and their coefficients are estimated by the local least squares estimator (LLSE)-based learning. Because the raw dataset collected from Raman spectroscopy include high-dimensional variables over about three thousands, principal component analysis(PCA) is applied for the dimensional reduction. In addition, artificial bee colony(ABC), which is one of the evolutionary algorithm, is used in order to identify the architecture and parameters of the proposed network. In experiment, the proposed classifier sorts the three kinds of plastics which is the most largely discharged in the real world. The effectiveness of the proposed classifier is proved through a comparison of performance between dataset obtained from chemical analysis and entire dataset extracted directly from Raman spectroscopy. |