Predicting with Confidence – An Improved Dynamic Cell Structure

• Published in: Advances in Natural Computation pp. 750 - 759
• Main Authors: Liu, Yan, Cukic, Bojan, Jiang, Michael, Xu, Zhiwei
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; Computer systems and distributed systems. User interface; Exact sciences and technology; Local Error; Prediction Performance; Radial Basis Function; Software; Validity Index; Voronoi Region; Performance evaluation; Cell structure; Validation; Flight; Cell network; Distributed system; Adaptive method; Confidence interval; Case study; Self organization; Confidence; Safety-critical software; Reliability; Learning algorithm; Artificial intelligence
• ISBN: 3540283234; 9783540283232
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11539087_99

As 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.