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Uncertainty propagation through radial basis function networks part I: regression networks

Chetwynd, D. and Worden, K. and Manson, G. and Pierce, S.G. (2005) Uncertainty propagation through radial basis function networks part I: regression networks. In: Eurodyn 2005: 6th International Conference on Structural Dynamics, 2005-09-04 - 2005-09-07, Paris.

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Abstract

Radial Basis Function (RBF) networks are examples of a versatile artificial neural network paradigm which lend themselves equally well to problems of classification and regression. Training the networks can be accomplished by a number of textbook techniques. The objective of the current paper is to explore how uncertainty propagates through such networks. In this, the first of two papers, the regression problem is addressed. The RBF networks are trained with crisp data, but interval output weights, in such a way that a regression model predicts an interval rather than a crisp value. This technique, as developed for the more common Multi-Layer Perceptron (MLP) network allows the user to investigate Ben-Haim’s concept of opportunity.

Item type: Conference or Workshop Item (Paper)
ID code: 11775
Notes: Requires Template change to Chapter in Book/Report/Conference proceeding › Conference contribution
Keywords: multi-layer perceptron network, Radial Basis Function, Ben-Haim’s concept of, artificial neural 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 15:07
Last modified: 17 Jul 2013 15:19
URI: http://strathprints.strath.ac.uk/id/eprint/11775

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