System identification
Local feedback multilayered networks
Neural Computation
Self-organization of nets of active neurons
Systems Analysis Modelling Simulation
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Self-Organizing Methods in Modeling: Gmdh Type Algorithms
Bounding Approaches to System Identification
Bounding Approaches to System Identification
Neural Networks for Identification, Prediction, and Control
Neural Networks for Identification, Prediction, and Control
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Static and Dynamic Neural Networks: From Fundamentals to Advanced Theory
Fault Diagnosis: Models, Artificial Intelligence, Applications
Fault Diagnosis: Models, Artificial Intelligence, Applications
Design of an analytic constrained predictive controller using neural networks
International Journal of Systems Science
Diagnosis and Fault-Tolerant Control
Diagnosis and Fault-Tolerant Control
Issues of Fault Diagnosis for Dynamic Systems
Issues of Fault Diagnosis for Dynamic Systems
Editorial: Introduction to the special issue on neural network feedback control
Automatica (Journal of IFAC)
IEEE Transactions on Neural Networks
Improving heat exchanger supervision using neural networks and rule based techniques
Expert Systems with Applications: An International Journal
Robust sensor and actuator fault diagnosis with GMDH neural networks
IWANN'13 Proceedings of the 12th international conference on Artificial Neural Networks: advances in computational intelligence - Volume Part I
Passive robust fault detection using RBF neural modeling based on set membership identification
Engineering Applications of Artificial Intelligence
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This article deals with the problem of determination of the model uncertainty during the system identification via application of the self-organising group method of data handling (GMDH) neural network. In particular, the contribution of the neural network structure errors and the parameter estimates inaccuracy to the model uncertainty were presented. Knowing these sources and applying the Outer Bounding Ellipsoid (OBE) algorithm it was possible to calculate the uncertainty of the parameters and the model output. The mathematical description of the model uncertainty enabled designing the robust fault detection system, whose effectiveness was verified by the DAMADICS benchmark.