Uncertainty propagation through radial basis function networks part II: classification networks

S.G. Pierce, K. Worden, G. Manson

Research output: Contribution to conferencePaper

Abstract

Radial basis function neural networks were trained using both partially supervised and fully supervised training techniques on a simple two-dimensional Gaussian data set. Forward uncertainty propagation through these networks was assessed using a technique of nested interval sets to form an information-gap model of classification performance of the networks. We demonstrate that the interval technique allows both the quantification of worst case and best case error performance of an individual network; and additionally provides an effective tool for optimal network selection in the presence of uncertainty.
Original languageEnglish
Pages929-934
Number of pages5
Publication statusPublished - 2005

Keywords

  • Radial Basis Function
  • artificial neural network
  • Ben-Haim’s concept of
  • multi-layer perceptron network

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