Performance estimation of a neural network-based controller

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.