Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Topology representing networks
Neural Networks
Self-organizing maps
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner's Handbook
Validating a neural network-based online adaptive system
Validating a neural network-based online adaptive system
HASE'04 Proceedings of the Eighth IEEE international conference on High assurance systems engineering
Predicting with confidence – an improved dynamic cell structure
ICNC'05 Proceedings of the First international conference on Advances in Natural Computation - Volume Part I
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Biologically inspired soft computing paradigms such as neural networks are popular learning models adopted in adaptive control systems for their ability to cope with a changing environment. However, continual changes induce uncertainty that limits the applicability of conventional validation techniques to assure a reliable system performance. In this paper, we present a dynamic approach to estimate the performance of two types of neural networks employed in an adaptive flight controller: the validity index for the outputs of a Dynamic Cell Structure (DCS) network and confidence levels for the outputs of a Sigma-Pi (or MLP) network. Both tools provide statistical inference of the neural network predictions and an estimate of the current performance of the network. We further evaluate how the quality of each parameter of the network (e.g., weight) influences the output of the network by defining a metric for parameter sensitivity and parameter confidence for DCS and Sigma-Pi networks. Experimental results on the NASA F-15 flight control system demonstrate that our techniques effectively evaluate the network performance and provide validation inferences in a real-time manner.