Predicting with Confidence – An Improved Dynamic Cell Structure

Abstract

AbstractAs a special type of Self-Organizing Maps, the Dynamic Cell Structures (DCS) network has topology-preserving adaptive learning capabilities that can, in theory, respond and learn to abstract from a much wider variety of complex data manifolds. However, the highly complex learning algorithm and non-linearity behind the dynamic learning pattern pose serious challenge to validating the prediction performance of DCS and impede its spread in control applications, safety-critical systems in particular.In this paper, we improve the performance of DCS networks by providing confidence measures on DCS predictions. We present the validity index, an estimated confidence interval associated with each DCS output, as a reliability-like measure of the network’s prediction performance. Our experiments using artificial data and a case study on a flight control application demonstrate an effective validation scheme of DCS networks to achieve better prediction performance with quantified confidence measures.KeywordsRadial Basis FunctionLocal ErrorPrediction PerformanceValidity IndexVoronoi RegionThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.