Abstract
Artificial neural networks of the dynamic type provide an excellent mathematical tool for dealing with non-linear dynamic problems. There are many application domains where the accurate model of a process/plant plays key role. One of the most stimulating practical examples is Fault Detection and Identification (FDI) of industrial systems [1]. Preparation of experimental conditions in order to collect informative measurements can be very expensive and the data acquired form real-world system may be also very noisy, therefore using all the available data may lead to significant systematic modelling errors.
This work was supported in part by the Ministry of Science and Higher Education in Poland under the grants N N514 1219 33 and N N514 2305 37.
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Patan, K., Patan, M. (2010). Selection of Training Data for Locally Recurrent Neural Network. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6353. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15822-3_16
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DOI: https://doi.org/10.1007/978-3-642-15822-3_16
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