Part of Advances in Neural Information Processing Systems 3 (NIPS 1990)
Kenneth Marko
Diagnosis of faults in complex, real-time control systems is a complicated task that has resisted solution by traditional methods. We have shown that neural networks can be successfully employed to diagnose faults in digitally controlled powertrain systems. This paper discusses the means we use to develop the appropriate databases for training and testing in order to select the optimum network architectures and to provide reasonable estimates of the classification accuracy of these networks on new samples of data. Recent work applying neural nets to adaptive control of an active suspension system is presented.