Abstract
This paper is focused on developing more efficient computational schemes for modeling in biochemical processes. A theoretical framework for estimation of process kinetic rates based on different temporal (time accounting) Artificial Neural Network (ANN) architectures is introduced. Three ANNs that explicitly consider temporal aspects of modeling are exemplified: i) Recurrent Neural Network (RNN) with global feedback (from the network output to the network input); ii) Time Lagged Feedforward Neural Network (TLFN) and iii) Reservoir Computing Network (RCN). Crystallization growth rate estimation is the benchmark for testing the methodology. The proposed hybrid (dynamical ANN & analytical submodel) schemes are promising modeling framework when the process is strongly nonlinear and particularly when input-output data is the only information available.
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References
Antonelo, E.A., Schrauwen, B., Campenhout, J.V.: Generative modeling of autonomous robots and their environments using reservoir computing. Neural Processing Letters 26(3), 233–249 (2007)
Bastin, G., Dochain, D.: On-line estimation and adaptive control of bioreactors. Elsevier Science Publishers, Amsterdam (1990)
Bishop, C.M.: Pattern Recognition and Machine Learning. Springer, Heidelberg (2006)
Chen, L., Bastin, G.: Structural identifiability of the yeals coefficients in bioprocess models when the reaction rates are unknown. Mathematical Biosciences 132, 35–67 (1996)
Georgieva, P., Meireles, M.J., Feyo de Azevedo, S.: Knowledge Based Hybrid Modeling of a Batch Crystallization When Accounting for Nucleation, Growth and Agglomeration Phenomena. Chem. Eng. Science 58, 3699–3707 (2003)
Hagan, M.T., Demuth, H.B., Beale, M.H.: Neural Network Design. PWS Publishing, Boston (1996)
Haykin, S.: Neural Networks: A Comprehensive Foundation. Prentice-Hall, NJ (1999)
Jaeger, H.: The “echo state” approach to analysing and training recurrent neural networks. Technical Report GMD Report 148, German National Research Center for Information Technology (2001a)
Maass, W., Natschlager, T., Markram, H.: Real-time computing without stable states: A new framework for neural computation based on perturbations. Neural Computation 14(11), 2531–2560 (2002)
Mandic, D.P., Chambers, J.A.: Recurrent Neural Networks for Prediction: Learning Algorithms, Architectures and Stability (Adaptive & Learning Systems for Signal Processing, Communications & Control). Wiley, Chichester (2001)
Noykove, N., Muller, T.G., Gylenberg, M., Timmer, J.: Quantitative analysis of anaerobic wastewater treatment processes: identifiably and parameter estimation. Biotechnology and bioengineering 78(1), 91–103 (2002)
Oliveira, C., Georgieva, P., Rocha, F., Feyo de Azevedo, S.: Artificial Neural Networks for Modeling in Reaction Process Systems. In: Neural Computing & Applications, vol. 18, pp. 15–24. Springer, Heidelberg (2009)
Principe, J.C., Euliano, N.R., Lefebvre, W.C.: Neural and adaptive systems: Fundamentals through simulations, New York (2000)
Steil, J.J.: Backpropagation-Decorrelation: Online recurrent learning with O(N) complexity. In: Proc. Int. Joint Conf. on Neural Networks (IJCNN), vol. 1, pp. 843–848 (2004)
Walter, E., Pronzato, L.: Identification of parametric models from experimental data. Springer, UK (1997)
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Georgieva, P., Suárez, L.A.P., de Azevedo, S.F. (2010). Time Accounting Artificial Neural Networks for Biochemical Process Models. In: Sgurev, V., Hadjiski, M., Kacprzyk, J. (eds) Intelligent Systems: From Theory to Practice. Studies in Computational Intelligence, vol 299. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13428-9_8
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DOI: https://doi.org/10.1007/978-3-642-13428-9_8
Publisher Name: Springer, Berlin, Heidelberg
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