Scholarlink

With the increasing dependence of the logistics industry on plastics, the amount of plastic waste has gradually risen, leading to environmental problems and resource wastage. Due to the high absorption of visible light and near-infrared wavelengths by black plastics, most common optical technologies struggle to effectively sort black plastics, and traditional manual classification methods are inefficient and inaccurate. To address this issue, we propose an efficient algorithm for the identification of black plastics. First, Raman spectroscopy is used to analyze black plastic and obtain relevant data. Then, principal component analysis (PCA) is applied to reduce the dimensionality of the spectral data and extract effective features. Next, a classification algorithm combining Radial Basis Function Neural Network (RBFNN) and Neural Gas Network (NGN) clustering is designed. This algorithm integrates the adaptive learning ability and pattern recognition capability of neural networks, enabling efficient classification of black plastics. Experiments were conducted using a Raman dataset, and the proposed algorithm was compared with three methods: RBFNN based on MinMax, Fuzzy C-Means (FCM), and Hard C-Means Clustering (HCM). Additionally, experiments were also performed on the publicly available datasets of Glass, Abalone, MyHog, and Page. The experimental results show that the proposed algorithm demonstrates superior performance across all datasets.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Design of Radial Basis Function Neural Network with Neural Gas Network Clustering for Classification of Logistics Black Plastics

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • Hongliang Yu,
  • Kun Zhou,
  • Ming Guo,
  • Xiangyong Chen,
  • Jianlong Qiu

摘要

With the increasing dependence of the logistics industry on plastics, the amount of plastic waste has gradually risen, leading to environmental problems and resource wastage. Due to the high absorption of visible light and near-infrared wavelengths by black plastics, most common optical technologies struggle to effectively sort black plastics, and traditional manual classification methods are inefficient and inaccurate. To address this issue, we propose an efficient algorithm for the identification of black plastics. First, Raman spectroscopy is used to analyze black plastic and obtain relevant data. Then, principal component analysis (PCA) is applied to reduce the dimensionality of the spectral data and extract effective features. Next, a classification algorithm combining Radial Basis Function Neural Network (RBFNN) and Neural Gas Network (NGN) clustering is designed. This algorithm integrates the adaptive learning ability and pattern recognition capability of neural networks, enabling efficient classification of black plastics. Experiments were conducted using a Raman dataset, and the proposed algorithm was compared with three methods: RBFNN based on MinMax, Fuzzy C-Means (FCM), and Hard C-Means Clustering (HCM). Additionally, experiments were also performed on the publicly available datasets of Glass, Abalone, MyHog, and Page. The experimental results show that the proposed algorithm demonstrates superior performance across all datasets.