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Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification

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Abstract

Polynomial neural networks have been known to exhibit useful properties as classifiers and universal approximators. In this study, we introduce a concept of polynomial-based radial basis function neural networks (P-RBF NNs), present a design methodology and show the use of the networks in classification problems. From the conceptual standpoint, the classifiers of this form can be expressed as a collection of “if-then” rules. The proposed architecture uses two essential development mechanisms. Fuzzy clustering (Fuzzy C-Means, FCM) is aimed at the development of condition parts of the rules while the corresponding conclusions of the rules are formed by some polynomials. A detailed learning algorithm for the P-RBF NNs is developed. The proposed classifier is applied to two-class pattern classification problems. The performance of this classifier is contrasted with the results produced by the “standard” RBF neural networks. In addition, the experimental application covers a comparative analysis including several previous commonly encountered methods such as standard neural networks, SVM, SOM, PCA, LDA, C4.5, and decision trees. The experimental results reveal that the proposed approach comes with a simpler structure of the classifier and better prediction capabilities.

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Authors and Affiliations

  1. Telematics & USN Research Department, Electronics and Telecommunications Research Institute (ETRI), 138 Gajeongno, Yuseong-gu, Daejeon, 305-700, South Korea

    Byoung-Jun Park

  2. Department of Electrical & Computer Engineering, University of Alberta, Edmonton, AB, T6R 2G7, Canada

    Witold Pedrycz

  3. Systems Science Institute, Polish Academy of Sciences, 6 Newelska 6, Warsaw, Poland

    Witold Pedrycz

  4. Department of Electrical Engineering, University of Suwon, Hwaseong-si, Gyeonggi-do, South Korea

    Sung-Kwun Oh

Authors
  1. Byoung-Jun Park
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  2. Witold Pedrycz
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  3. Sung-Kwun Oh
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Correspondence to Byoung-Jun Park.

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Park, BJ., Pedrycz, W. & Oh, SK. Polynomial-based radial basis function neural networks (P-RBF NNs) and their application to pattern classification. Appl Intell 32, 27–46 (2010). https://doi.org/10.1007/s10489-008-0133-z

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  • DOI: https://doi.org/10.1007/s10489-008-0133-z

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