Abstract
This work details development of dynamic neural models of a yeast fermentation chemical reactor using Extreme Learning Machines (ELM). The ELM approach calculates very efficiently, without nonlinear optimisation, dynamic models, but only in the non-recurrent serial-parallel configuration. It is shown that in the case of the considered benchmark the ELM technique gives models which are also quite good recurrent long-range predictors, they work in the parallel configuration (simulation mode). Furthermore, properties of neural models obtained by the ELM and classical (optimisation-based) approaches are compared.
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Ławryńczuk, M. (2016). Neural Modelling of a Yeast Fermentation Process Using Extreme Learning Machines. In: Szewczyk, R., Zieliński, C., Kaliczyńska, M. (eds) Challenges in Automation, Robotics and Measurement Techniques. ICA 2016. Advances in Intelligent Systems and Computing, vol 440. Springer, Cham. https://doi.org/10.1007/978-3-319-29357-8_2
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DOI: https://doi.org/10.1007/978-3-319-29357-8_2
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