Picture of a black hole

Strathclyde Open Access research that creates ripples...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of research papers by University of Strathclyde researchers, including by Strathclyde physicists involved in observing gravitational waves and black hole mergers as part of the Laser Interferometer Gravitational-Wave Observatory (LIGO) - but also other internationally significant research from the Department of Physics. Discover why Strathclyde's physics research is making ripples...

Strathprints also exposes world leading research from the Faculties of Science, Engineering, Humanities & Social Sciences, and from the Strathclyde Business School.

Discover more...

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.