Topology representing networks
Neural Networks
Dynamic cell structure learns perfectly topology preserving map
Neural Computation
Adaptive Control Software: Can We Guarantee Safety?
COMPSAC '04 Proceedings of the 28th Annual International Computer Software and Applications Conference - Workshops and Fast Abstracts - Volume 02
Lyapunov analysis of neural network stability in an adaptive flight control system
SSS'03 Proceedings of the 6th international conference on Self-stabilizing systems
An approach to v&v of embedded adaptive systems
FAABS'04 Proceedings of the Third international conference on Formal Approaches to Agent-Based Systems
Performance estimation of a neural network-based controller
ISNN'06 Proceedings of the Third international conference on Advnaces in Neural Networks - Volume Part II
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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.