Higher-order-statistics-based radial basis function networks for signal enhancement

Bor-Shyh Lin, Bor Shing Lin, Fok Ching Chong, Feipei Lai

Research output: Contribution to journalArticlepeer-review

35 Scopus citations


In this paper, a higher-order-statistics (HOS)-based radial basis function (RBF) network for signal enhancement is introduced. In the proposed scheme, higher order cumulants of the reference signal were used as the input of HOS-based RBF. An HOS-based supervised learning algorithm, with mean square error obtained from higher order cumulants of the desired input and the system output as the learning criterion, was used to adapt weights. The motivation is that the HOS can effectively suppress Gaussian and symmetrically distributed non-Gaussian noise. The influence of a Gaussian noise on the input of HOS-based RBF and the HOS-based learning algorithm can be mitigated. Simulated results indicate that HOS-based RBF can provide better performance for signal enhancement under different noise levels, and its performance is insensitive to the selection of learning rates. Moreover, the efficiency of HOS-based RBF under the nonstationary Gaussian noise is stable.

Original languageEnglish
Pages (from-to)823-832
Number of pages10
JournalIEEE Transactions on Neural Networks
Issue number3
StatePublished - 1 May 2007


  • Gaussian noise
  • Higher order statistics (HOS)
  • Radial basis function (RBF) networks
  • Signal enhancement


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