Resisting the influence of outliers in radial basis function neural networks

Jea Rong Tsai, Pau Choo Chung, Chein I. Chang

研究成果: Paper

1 引文 (Scopus)

摘要

Radial basis function (RBF) neural networks were shown to be a promising network model in function approximation. Training an RBF network is usually approached based on a least square criterion, accompanied with an adaptive growing technique to determine the optimal size of network. With this approach, two problems usually arise when the training patterns contain outliers. Firstly, the least square would cause the network to incorrectly interpolate the outliers. Secondly, because of the interference of outliers, the number of nodes determined by traditional growing algorithm will stuck at a certain number, causing that the proper network size cannot be reached. In order to cope with the first problem, this paper proposes a method to construct a robust criterion function to replace with the least square criterion. For solving the second problem, the paper introduces a memory mechanism into the adaptive growing technique to restrain the influence of outliers. Simulation results indicate that the robust criterion function obtained using our method can effectively reduce the influence of outliers. Furthermore, with the incorporation of memory mechanism, a better size of network can be obtained.

原文English
頁面42-51
頁數10
出版狀態Published - 1996 一月 1
事件Proceedings of the 1996 IEEE Signal Processing Society Workshop - Kyota, Jpn
持續時間: 1996 九月 41996 九月 6

Other

OtherProceedings of the 1996 IEEE Signal Processing Society Workshop
城市Kyota, Jpn
期間96-09-0496-09-06

指紋

Neural networks
Data storage equipment
Radial basis function networks

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Software
  • Electrical and Electronic Engineering

引用此文

Tsai, J. R., Chung, P. C., & Chang, C. I. (1996). Resisting the influence of outliers in radial basis function neural networks. 42-51. 論文發表於 Proceedings of the 1996 IEEE Signal Processing Society Workshop, Kyota, Jpn, .
Tsai, Jea Rong ; Chung, Pau Choo ; Chang, Chein I. / Resisting the influence of outliers in radial basis function neural networks. 論文發表於 Proceedings of the 1996 IEEE Signal Processing Society Workshop, Kyota, Jpn, .10 p.
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Tsai, JR, Chung, PC & Chang, CI 1996, 'Resisting the influence of outliers in radial basis function neural networks', 論文發表於 Proceedings of the 1996 IEEE Signal Processing Society Workshop, Kyota, Jpn, 96-09-04 - 96-09-06 頁 42-51.

Resisting the influence of outliers in radial basis function neural networks. / Tsai, Jea Rong; Chung, Pau Choo; Chang, Chein I.

1996. 42-51 論文發表於 Proceedings of the 1996 IEEE Signal Processing Society Workshop, Kyota, Jpn, .

研究成果: Paper

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N2 - Radial basis function (RBF) neural networks were shown to be a promising network model in function approximation. Training an RBF network is usually approached based on a least square criterion, accompanied with an adaptive growing technique to determine the optimal size of network. With this approach, two problems usually arise when the training patterns contain outliers. Firstly, the least square would cause the network to incorrectly interpolate the outliers. Secondly, because of the interference of outliers, the number of nodes determined by traditional growing algorithm will stuck at a certain number, causing that the proper network size cannot be reached. In order to cope with the first problem, this paper proposes a method to construct a robust criterion function to replace with the least square criterion. For solving the second problem, the paper introduces a memory mechanism into the adaptive growing technique to restrain the influence of outliers. Simulation results indicate that the robust criterion function obtained using our method can effectively reduce the influence of outliers. Furthermore, with the incorporation of memory mechanism, a better size of network can be obtained.

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Tsai JR, Chung PC, Chang CI. Resisting the influence of outliers in radial basis function neural networks. 1996. 論文發表於 Proceedings of the 1996 IEEE Signal Processing Society Workshop, Kyota, Jpn, .