Abstract
The increasing complexity of technological processes implemented in present industrial installations causes serious problems in the modern control system design and analysis. Chemical refineries, electrical furnaces, water treatments and other industrial plants are complex systems and in some cases cannot be precisely described by classical mathematical models. On the other hand, modern industrial systems are subject to faults in their components. Due to these facts, fault-tolerant control design using soft computing methods is gaining more and more attention in recent years. In this paper, the model-based approach to fault detection and isolation using locally recurrent neural networks is presented. The paper contains a numerical example that illustrates the performance of the proposed locally recurrent neural network with respect to other well-known neural structures.
The research presented in the paper has been partially supported by the Ministry of Science and Higher Education under Grant No. N N514 3412 33.
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Przystałka, P. (2008). Model-Based Fault Detection and Isolation Using Locally Recurrent Neural Networks. In: Rutkowski, L., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing – ICAISC 2008. ICAISC 2008. Lecture Notes in Computer Science(), vol 5097. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69731-2_13
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DOI: https://doi.org/10.1007/978-3-540-69731-2_13
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