Applied Intelligence

, Volume 49, Issue 3, pp 929–949 | Cite as

Design of fuzzy radial basis function neural network classifier based on information data preprocessing for recycling black plastic wastes: comparative studies of ATR FT-IR and Raman spectroscopy

  • Jong-Soo Bae
  • Sung-Kwun OhEmail author
  • Witold Pedrycz
  • Zunwei Fu


As large amounts of plastics are widely used in diverse areas of industry, the amount of plastic waste, including black plastics, continues to increase. In this situation, the necessity of useful recycling having limited resources gradually increases. The design of plastic classification systems for plastics recycling becomes more important to effectively address recycling activities. Until now, conventional sorting systems based on the near infrared ray technology have been used to classify plastic wastes. However, the classification of black plastic waste still remains a challenge because such materials do not reflect sufficient signals due to the absorption of laser light coming from the NIR spectrometer. In order to solve such problems, this research is focused on an efficient way to identify black plastics. Attenuated Total Reflectance (ATR) Fourier Transform Infrared Radiation (FT-IR) and a Raman spectrometer are used to carry out qualitative and quantitative analysis for the effective as well as efficient classification of black plastic wastes. In this study, to effectively classify the black plastic waste, data processing and Fuzzy Transform (F-Transform) as well as PCA-based Fuzzy Radial Basis Function Neural Networks (FRBFNNs) classifier is proposed. Input variables extracted on a basis of chemical characteristic peaks as well as interval range positioned near the chemical characteristic peaks were exploited as a way to improve the classification performance of the FRBFNN classifier. In order to evaluate the performance of the classifier, a suite of techniques including F-Transform-based as well as Principal Component Analysis (PCA)-based FRBFNNs classifier designed with the aid of Particle Swam Optimization are developed to analyze and classify black plastics.


ATR FT-IR Raman Spectroscopy F-Transform-based and PCA-based Fuzzy Radial Basis Function Neural Networks (FRBFNN) classifier Recycling Black plastic wastes Particle swam optimization (PSO) 



This work was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education under Grant NRF-2017R1D1A1B03032333.


  1. 1.
    Hardesty BD, Chris W Eight million tonnes of plastic are going into the ocean each year. The Conversation, Retrieved 21 February 2015Google Scholar
  2. 2.
  3. 3.
    Ocean Optics (2017) ID Raman reader, Accessed 8 March 2017
  4. 4.
    Martín-Gil J, Palacios-Leblé G, Ramos PM, Martin-Gil FJ (2007) Analysis of a Celtiberian protective paste and its possible use by Arevaci warriors. J Interdiscip Celtic Stud 5:63–76Google Scholar
  5. 5.
    Gardiner DJ (1989) Practical Raman spectroscopy. Springer, ISBN 978-0-387-50254-0Google Scholar
  6. 6.
    Princeton Instruments, Raman Spectroscopy Basics, Princeton Instruments, New Jersey, USA, Raman Basics - Application notesGoogle Scholar
  7. 7.
    GnoSys Global Ltd, PET Analysis, GnoSys Global Ltd, Guildford, Surrey, TSAN11-Application notesGoogle Scholar
  8. 8.
    De Baez MA, Hendra PJ, Judkins M (1995) The Raman spectra of oriented isotactic polypropylene. Spectrochimica Acta Part A: Molecular Biomolecular Spectroscopy 51(12):2117–2124CrossRefGoogle Scholar
  9. 9.
    Andreassen E (1999) Infrared and Raman spectroscopy of polypropylene. In: Polypropylene. Springer, Netherlands, pp 320–328Google Scholar
  10. 10.
    Anema JR, Brolo AG, Felten A, Bittencourt C (2010) Surface-enhanced Raman scattering from polystyrene on gold clusters. J Raman Spectrosc 41.7:745–751Google Scholar
  11. 11.
    Zhang DH, Qin JG, Shen JS, Wang Y, Liu WJ (2000) Study on the concentration dependence of orientation of polystyrene on silver by the sers technique 18(2):177–180Google Scholar
  12. 12.
    Mazilu M, De Luca AC, Riches A, Herrington CS, Dholakia K (2010) Optimal algorithm for fluorescence suppression of modulated Raman spectroscopy. Opt Express 18.11:11382–11395CrossRefGoogle Scholar
  13. 13.
    Perfilieva I (2006) Fuzzy transform: theory and application. Fuzzy Sets Syst 175:993–1023MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Smith L (2002) A tutorial on principal components analysis. Cornell Univ USA 51.52:65Google Scholar
  15. 15.
    Yoo S-H, Oh S-K, Pedrycz W (2015) Optimized face recognition algorithm using radial basis function neural networks and its practical applications. Neural Netw 69:111–125CrossRefGoogle Scholar
  16. 16.
    Oh S-K, et al. (2012) Design of K-means clustering-based polynomial radial basis function neural networks(pRBFNNs) realized with the aid of particle swarm optimization and differential evolution. Neurocomputing 78.1:121–132CrossRefGoogle Scholar
  17. 17.
    Huang W, Oh S-K, Pedrycz W (2014) Design of hybrid radial basis function neural networks (HRBFNNs) realized with the aid of hybridization of fuzzy clustering method (FCM) and polynomial neural networks (PNNs). Neural Netw 60:166–181CrossRefGoogle Scholar
  18. 18.
    Abido MA (2002) Optimal design of power-system stabilizers using particle swarm optimization. IEEE Trans Energy Conv 17:406–413CrossRefGoogle Scholar
  19. 19.
    Frank E, Hall MA, Witten IH (2016) The WEKA workbench. Online appendix for data mining: practical machine learning tools and techniques, 2nd edn. Morgan Kaufmann, Burlington. Google Scholar
  20. 20.
    Wu D, Warwick K, Ma Z, Burgess JG, Pan S, Aziz TZ (2010) Prediction of Parkinson’s disease tremor onset using radial basis function neural networks. Expert Syst Appl 37(4):2923–2928CrossRefGoogle Scholar
  21. 21.
    Oh S-K, Park H-S, Kim W-D, Pedrycz W (2013) A new approach to radial basis function-based polynomial neural networks: analysis and design. Knowl Inf Syst 29(1):203–221Google Scholar
  22. 22.
    Roh S-B, Oh S-K (2014) Polynomial fuzzy radial basis function neural networks classifier realized with the aid of boundary area decision. J Electr Eng Technol 9(6):2098–2106CrossRefGoogle Scholar
  23. 23.
    Huang W, Oh S-K, Pedrycz W (2017) Hybrid fuzzy polynomial neural networks with the aid of weighted fuzzy clustering method and fuzzy polynomial. Appl Intell 46:487–508CrossRefGoogle Scholar
  24. 24.
    Park B-J, Pedrycz W, Oh S-K (2010) Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification. Appl Intell 32(1):27–46CrossRefGoogle Scholar
  25. 25.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines. Cambridge Univ Press, CambridgezbMATHGoogle Scholar
  26. 26.
    Tian Y e, et al. (2017) A new fuzzy set and nonkernel SVM approach for mislabeled binary classification with applications. IEEE Trans Fuzzy Syst 25(6):1536–1545CrossRefGoogle Scholar
  27. 27.
    Palm RB (2012) Prediction as a candidate for learning deep hierarchical models of data technical. University of DenmarkGoogle Scholar
  28. 28.
    Cristianini N, Shawe-Taylor J (2000) An introduction to support vector machines and other kernel-based learning methods, 1st edn. Cambridge University Press, Cambridge. CrossRefzbMATHGoogle Scholar
  29. 29.
    Kavukcuoglu K, Sermanet P, Boureau Y-L, Gregor K, Mathieu M, LeCun Y (2010) Learning convolutional feature hierachies for visual recognition. In: Advances in neural information processing systems (NIPS)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Electrical EngineeringThe University of SuwonGyeonggi-doSouth Korea
  2. 2.Key Laboratory of Complex systems and Intelligent Computing in Universities of ShandongLinyi UniversityLinyiChina
  3. 3.Department of Electrical & Computer EngineeringUniversity of AlbertaEdmontonCanada
  4. 4.Systems Research InstitutePolish Academy of SciencesWarsawPoland
  5. 5.Department of Electrical and Computer Engineering, Faculty of EngineeringKing Abdulaziz UniversityJeddahSaudi Arabia
  6. 6.Department of Computer ScienceThe University of SuwonGyeonggi-doSouth Korea

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