Izvorni znanstveni članak
https://doi.org/10.15255/CABEQ.2014.2090
Automated Classification of Bioprocess Based on Optimum Compromise Whitening and Clustering
J. Kukal
; Faculty of Chemical Engineering, University of Chemical Technology in Prague, Technicka 5, 166 28 Prague 6, Czech Republic
J. Mareš
; Faculty of Chemical Engineering, University of Chemical Technology in Prague, Technicka 5, 166 28 Prague 6, Czech Republic
J. Náhlík
; Faculty of Chemical Engineering, University of Chemical Technology in Prague, Technicka 5, 166 28 Prague 6, Czech Republic
P. Hrnčiřík
; Faculty of Chemical Engineering, University of Chemical Technology in Prague, Technicka 5, 166 28 Prague 6, Czech Republic
M. Klimt
; Faculty of Chemical Engineering, University of Chemical Technology in Prague, Technicka 5, 166 28 Prague 6, Czech Republic
Sažetak
The proposed methodology of technological state classification is based on data smoothing, dimensionality reduction, compromise whitening, and optimum clustering. The novelty of our approach is in the stabile state hypothesis which improves initialization of c-mean algorithm and enables interleaved cross-validation strategy. We also employ the Akaike information criterion to obtain the optimum number of technological
states that minimize it, but using as many as possible clusters and components. The general approach is applied to state classification of Pseudomonas putida fed-batch cultivation on octanoic acid.
Ključne riječi
fed-batch cultivation; technological state classification; dimensionality reduction; clustering; stabile state hypothesis
Hrčak ID:
150644
URI
Datum izdavanja:
3.1.2016.
Posjeta: 932 *