Neural Modelling of a Yeast Fermentation Process Using Extreme Learning Machines
AbstractThis 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.
|Publication size in sheets||0.5|
|Book||Szewczyk Roman, Kaliczyńska Małgorzata, Zieliński Cezary: Challenges in Automation, Robotics and Measurement Techniques. Proceedings of AUTOMATION-2016, March 2-4, 2016, Warsaw, Poland, Advances in Intelligent Systems and Computing, vol. 440, 2016, Springer International Publishing, ISBN 978-3-319-29356-1, [978-3-319-29357-8], 919 p., DOI:10.1007/978-3-319-29357-8|
|Keywords in English||Neural networks Extreme learning machines|
|project||Development of methodology of control, decision support and production management. Project leader: Zieliński Cezary,
, Phone: 5102, start date 19-05-2015, end date 31-12-2016, 504/02233/1031, Completed
|Score|| = 15.0, 27-03-2017, BookChapterSeriesAndMatConf|
= 15.0, 27-03-2017, BookChapterSeriesAndMatConf
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