Picture of UK Houses of Parliament

Leading national thinking on politics, government & public policy through Open Access research

Strathprints makes available scholarly Open Access content by researchers in the School of Government & Public Policy, based within the Faculty of Humanities & Social Sciences.

Research here is 1st in Scotland for research intensity and spans a wide range of domains. The Department of Politics demonstrates expertise in understanding parties, elections and public opinion, with additional emphases on political economy, institutions and international relations. This international angle is reflected in the European Policies Research Centre (EPRC) which conducts comparative research on public policy. Meanwhile, the Centre for Energy Policy provides independent expertise on energy, working across multidisciplinary groups to shape policy for a low carbon economy.

Explore the Open Access research of the School of Government & Public Policy. Or explore all of Strathclyde's Open Access research...

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.

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

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.