Abstract
Grey-box neural models mix differential equations, which act as white boxes, and neural networks, used as black boxes. The purpose of the present work is to show the training of a grey-box model by means of indirect backpropagation and Levenberg-Marquardt in Matlab®, extending the black box neural model in order to fit the discretized equations of the phenomenological model. The obtained grey-box model is tested as an estimator of a state variable of a biotechnological batch fermentation process on solid substrate, with good results.
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Cruz, F., Acuña, G., Cubillos, F., Moreno, V., Bassi, D. (2007). Indirect Training of Grey-Box Models: Application to a Bioprocess. In: Liu, D., Fei, S., Hou, Z., Zhang, H., Sun, C. (eds) Advances in Neural Networks – ISNN 2007. ISNN 2007. Lecture Notes in Computer Science, vol 4492. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-72393-6_47
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DOI: https://doi.org/10.1007/978-3-540-72393-6_47
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-72392-9
Online ISBN: 978-3-540-72393-6
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