Neuro-fuzzy prediction of uricase production |
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Authors: | S. Vassileva B. Tzvetkova C. Katranoushkova L. Losseva |
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Affiliation: | Institute of Control and Systems Research – BAS, Acad. G. Bonchev str., bl. 2, P.O. Box 79, 1113 Sofia, Bulgaria, BG Institute of Microbiology – BAS, Acad. G. Bonchev str., bl. 26, 1113 Sofia, Bulgaria, BG
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Abstract: | Recent biotechnology requires implementation of new modelling methods based on knowledge principles and learning structures, comprised in fuzzy knowledge-based systems (FKBS), neural networks (NN) and different hybrid methods. The intelligent modelling approaches solve sufficiently a very important problem - processing of scarce, uncertainty and incomplete numerical and linguistic information about multivariate non-linear and non-stationary systems as well as biotechnological processes. The paper deals with prediction of an enzyme oxidizing uric acid to alantoin - the uricase, produced by Candida utilis 90-12 employing neuro-fuzzy knowledge-based approach. The implemented predictive technique exploits the fact that the fuzzy model can be seen as a network structure, similar to artificial NN, which on computational level assure a high model accuracy. The predictors implemented are four different by nature variables. The developed predictive model shows that best predictors of uricase production are biomass and limiting substrate concentrations. |
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