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.

中文翻译：

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基于模糊RBF神经网络的黑色塑料识别：聚焦傅里叶变换红外辐射数据预处理技术


提高各种塑料材料的系统识别性能以有效回收废塑料是本文研究的关键问题。对于含有炭黑的黑色塑料，人们无法将其与其他材料区分开来。为了促进识别过程，使用具有衰减全反射率的傅里叶变换红外对黑色塑料进行定性和定量分析。由于以这种方式获得的频谱构成高维数据，因此需要进行特征约简来提取声音特征并降低原始频谱的维度。在本研究中，考虑了三种类型的特征提取技术：峰值检测技术、基于化学特征的特征提取以及基于模糊变换的特征提取以确定声音判别特征。为了增强分类过程，构建了模糊径向基函数神经网络分类器；这些分类器的架构利用了混合技术。基于实验研究，表明所提出的具有特征提取技术的分类系统比已经研究的分类器报告的性能表现出更优越的性能。