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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.

Item type: Conference or Workshop Item (Paper)
ID code: 11768
Notes: Requires Template change to Chapter in Book/Report/Conference proceeding › Conference contribution
Keywords: Radial Basis Function, artificial neural network, Ben-Haim’s concept of, multi-layer perceptron network, Electrical engineering. Electronics Nuclear engineering
Subjects: Technology > Electrical engineering. Electronics Nuclear engineering
Department: Faculty of Engineering > Electronic and Electrical Engineering
Related URLs:
Depositing user: Strathprints Administrator
Date Deposited: 25 Jul 2011 15:16
Last modified: 17 Jul 2013 15:21
URI: http://strathprints.strath.ac.uk/id/eprint/11768

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