Abstract
The uncertainty of neural model influences the effectiveness of the neural model-based FDI and FTC systems. The application of the GMDH approach to the state-space neural model structure selection allows reducing the model uncertainty. The state-space representation of the neural model enables to develop a new technique of estimation of the neural model inputs based on the RUIF. This result enables performing robust fault detection and isolation of the actuators.
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Witczak, M., Mrugalski, M., Korbicz, J. (2013). Robust Sensor and Actuator Fault Diagnosis with GMDH Neural Networks. In: Rojas, I., Joya, G., Gabestany, J. (eds) Advances in Computational Intelligence. IWANN 2013. Lecture Notes in Computer Science, vol 7902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38679-4_8
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DOI: https://doi.org/10.1007/978-3-642-38679-4_8
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