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作者:

Han, H. (Han, H..) | Kuai, X. (Kuai, X..) | Zhang, L. (Zhang, L..) | Qiao, J. (Qiao, J..)

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摘要:

To obtain the accurate price of waste mobile phones in the recycling process, a value assessment method, based on the fuzzy neural network (FNN) was proposed in this paper. First, a characteristic extraction method was designed by using the principal component analysis (PCA) to obtain the key characteristic variables of price for recycling waste mobile phones. Second, a value assessment model was established by using a FNN to describe the nonlinear relationship between the recycling price and the key characteristic variables. Third, an adaptive second-order parameter learning algorithm (ASOPLA) was proposed to improve the adaptive capability of the value assessment model. Finally, the proposed value assessment method was applied to a real transaction process. Results demonstrate that this proposed value assessment method can obtain the accurate price of waste mobile phones. © 2019, Editorial Department of Journal of Beijing University of Technology. All right reserved.

关键词:

Adaptive second-order parameter learning algorithm; Fuzzy neural network (FNN); Principle component analysis (PCA); Recycling of waste mobile phones; Value assessment

作者机构:

  • [ 1 ] [Han, H.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 2 ] [Kuai, X.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 3 ] [Zhang, L.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China
  • [ 4 ] [Qiao, J.]Beijing Key Laboratory of Computational Intelligence and Intelligent System, Faculty of Information Technology, Beijing University of Technology, Beijing, 100124, China

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来源 :

Journal of Beijing University of Technology

ISSN: 0254-0037

年份: 2019

期: 11

卷: 45

页码: 1033-1040

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SCOPUS被引频次: 10

ESI高被引论文在榜: 0 展开所有

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