Constraint reasoning based on interval arithmetic: the tolerance propagation approach
Artificial Intelligence - Special volume on constraint-based reasoning
Neural networks for control systems: a survey
Automatica (Journal of IFAC)
Nonlinear black-box modeling in system identification: a unified overview
Automatica (Journal of IFAC) - Special issue on trends in system identification
Robust model-based fault diagnosis for dynamic systems
Robust model-based fault diagnosis for dynamic systems
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Identification and control of dynamical systems using neural networks
IEEE Transactions on Neural Networks
Toward the training of feed-forward neural networks with the D-optimum input sequence
IEEE Transactions on Neural Networks
Improving decision making in fault detection and isolation using model validity
Engineering Applications of Artificial Intelligence
Structure identification of Bayesian classifiers based on GMDH
Knowledge-Based Systems
A GMDH-based fuzzy modeling approach for constructing TS model
Fuzzy Sets and Systems
A robust missing value imputation method for noisy data
Applied Intelligence
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This paper proposes a new passive robust fault detection scheme using non-linear models that include parameter uncertainty. The non-linear model considered here is described by a group method of data handling (GMDH) neural network. The problem of passive robust fault detection using models including parameter uncertainty has been mainly addressed by checking if the measured behaviour is inside the region of possible behaviours based on the so-called forward test since it bounds the direct image of an interval function. The main contribution of this paper is to propose a new backward test, based on the inverse image of an interval function, that allows checking if there exists a parameter in the uncertain parameter set that is consistent with the measured system behaviour. This test is implemented using interval constraint satisfaction algorithms which can perform efficiently in deciding if the measured system state is consistent with the GMDH model and its associated uncertainty. Finally, this approach is tested on the servoactuator being a FDI benchmark in the European Project DAMADICS.