Picture of a sphere with binary code

Making Strathclyde research discoverable to the world...

The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. It exposes Strathclyde's world leading Open Access research to many of the world's leading resource discovery tools, and from there onto the screens of researchers around the world.

Explore Strathclyde Open Access research content

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

Pierce, S.G. and Worden, K. and Manson, G. (2005) Uncertainty propagation through radial basis function networks part II: classification networks. In: UNSPECIFIED.

Full text not available in this repository. (Request a copy from the Strathclyde author)

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.