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Feature data-driven-reinforced fuzzy radial basis function neural network classifier with the aid of preprocessing techniques and particle swarm optimization

  • Foundation, algebraic, and analytical methods in soft computing
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Abstract

In this study, reinforced fuzzy radial basis function neural networks (FRBFNN) classifier driven by feature extracted data completed with the aid of effectively preprocessing techniques and evolutionary optimization, and its comprehensive design methodology are introduced. An Overall structure of the reinforced FRBFNN comprises the preprocessing part, the premise part and the consequence part of fuzzy rules of the network. In the preprocessing part, four types of preprocessing algorithms such as principal component analysis (PCA), linear discriminant analysis (LDA), combination of PCA and LDA (Hybrid PCA) and fuzzy transform are considered. To extract feature data suitable to characterize signal data, the feature extraction of information data is carried out through the dimensionality reduction done by the preprocessing technique, and then the reduced data are used as the input to the FRBFNN classifier. In the premise part of fuzzy rules of the network, the number of fuzzy rules is determined according to the number of clusters by fuzzy c-means (FCM) clustering. The fitness values of individual fuzzy rules are obtained based on data distribution. In the consequence part of fuzzy rules of the network, the parameters of connection weights located between the hidden layer and the output layer of FRBFNN classifier are estimated by means of the least square estimation. Particle swarm optimization (PSO) is exploited for structural as well as parametric optimization in the FRBFNN classifier. The parameters to be optimized by PSO are related to six factors such as the determination of whether to use data preprocessing, the type of data preprocessing technique, the number of input variables reduced by the preprocessing technique, fuzzification coefficient and the number of fuzzy rules used in fuzzy c-means (FCM) clustering, and the type of connection weights. By using diverse benchmark dataset obtained from UCI repository, the classification performance of the reinforced FRBFNN classifier was evaluated. Through a variety of classification algorithms existed in the Weka data mining software (Weka), the classification performance of the reinforced FRBFNN classifier was compared as well. The superiority of the proposed classifier is demonstrated through Friedman test. Furthermore, we assessed the classification performance of the reinforced FRBFNN classifier applied to black plastic wastes spectral data acquired from Raman and Laser induced breakdown spectroscopy equipment for the practical application of the material sorting system of the black plastic wastes.

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Acknowledgements

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2021R1F1A1056102).

Funding

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MSIT) (NRF-2021R1F1A1056102 & NRF-2023K2A9A2A06060385).

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Authors and Affiliations

  1. School of Electrical & Electronic Engineering, The University of Suwon, 17 Wauan-gil, Bongdam-eup, Hwaseong-si, Gyeonggi-do, 18323, South Korea

    Sang-Beom Park & Sung-Kwun Oh

  2. Department of Electronic Engineering, Seokyeong University, Seoul, 02713, South Korea

    Sung-Kwun Oh

  3. Research Center for Big Data and Artificial Intelligence, Linyi University, Linyi, 276005, China

    Sung-Kwun Oh

  4. Department of Electrical & Computer Engineering, University of Alberta, Edmonton, T6R 2V4 AB, Canada

    Witold Pedrycz

  5. Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland

    Witold Pedrycz

  6. Istinye University, Faculty of Engineering and Natural Sciences, Department of Computer Engineering, Sariyer/Istanbul, Türkiye

    Witold Pedrycz

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  1. Sang-Beom Park
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Correspondence to Sung-Kwun Oh.

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Park, SB., Oh, SK. & Pedrycz, W. Feature data-driven-reinforced fuzzy radial basis function neural network classifier with the aid of preprocessing techniques and particle swarm optimization. Soft Comput 27, 15443–15462 (2023). https://doi.org/10.1007/s00500-023-09124-6

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