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The Strathprints institutional repository is a digital archive of University of Strathclyde research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including those from the School of Psychological Sciences & Health - but also papers by researchers based within the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

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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.

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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.