By M. N. Karim, S. L. Rivera (auth.)
Contents: M.N. Karim, S.L. Rivera, castle Collins, united states: Artificial Neural Networks in Bioprocess kingdom Estimation.- G.E. Grampp, G.N. Stephanopoulos, Cambridge, united states, A. Sambanis, Atlanta, united states: Use of Regulated Secretion of Protein creation from Animal Cells: An Overview.- I. Karube, T. Takeuchi, D.J. Barnes, Tokyo: Biotechnological Reduction of CO2 Emissions.- B. Mattiasson, H. Hakanson, Lund, Sweden: Immunochemically dependent Assays for procedure Control.- I. Endo, Wako-shi, Saitama, Japan: A Human Genome Analysis method (HUGA-I) built in Japan.- S. Shioya, Osaka, Japan: Optimization and keep watch over in Fed-Batch Bioreactors.- A. Fiechter, B. Sonnleitner, Zurich: Impacts of vehicle mated Bioprocess structures on glossy organic Research.- S. Furusaki, M. Seki, Tokyo: Use and Engineering Aspects of Immobilized Cells in Biotechnology.- J. Nielsen, Lyngby, Denmark: Modelling the expansion of Filamentous Fungi.- I. Economidis, Brussels: An evaluate of the Biotechnology Research actions within the ecu Community.-
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I i ! 01 Fig. 10. 001 10 20 30 Iterations 40 50 global minimum, since comparable SSE values were obtained after 50 iterations, but the network weights are not the same, not even in the sign. This suggests a highly convoluted error surface with several minima. The influence of varying the number of learning iterations was also investigated. Figure 10 shows the error function SSE for the training data and the test data set T -- 37 ~ Starting from initially random weights, the training was performed for 50 iterations.
In: Proceedings of the 1990 American Control Conference, San Diego, CA, May 23-25, p 2144 32. Swings J, De Ley J (1977) Bacteriological Reviews 41:1 33. Cybenko G (1989) Mathematics of Control, Signal and Systems 2:303 Artificial Neural Networks in Bioprocess State Estimation 33 34. Hertz J, Krogh A, Palmer RG (1991) Introduction to the Theory of Neural Computation, vol. I. Addison-Wesley, Redwood City, CA 35. Morris AJ, Montague GA, Tham MT, Aynsley M, Di Massimo C, Lant P (1991) Towards improved process supervision-algorithms and knowledge based systems.
The influence of time as an input to the net has not been reported for batch cultivations. Figure 13 shows estimations for the batch data set with T = 37 ~ when the time is included as an input to the net. 18. This suggests that the time dependency is important, especially in batch systems. Therefore, the temporal ! 4 10 0 0 2 4 6 Time, h 8 10 9 8 O Fig. 12. Neural network test data and estimations for the T = 37 ~ batch data set assuming no noise is present in the temperature measurements. 08.