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Description
| - The possibilities of artificial neural networks (ANNs) “soft” computing to evaluate chemical kinetic data have been studied. In the first stage, , a set of “standard” kinetic curves with known parameters (rate constants and/or concentrations of the reactants), which is some kind of “normalized maps”, is prepared. The data base should be built according to a suitable experimental design (ED). In the second stage, such data set is then used for ANNs “learning”. Afterwards, in the second stage, experimental data are evaluated and parameters of “other” kinetic curves are computed without solving anymore the system of differential equations. The combined ED-ANNs approach has been applied to solve several kinetic systems. It was also demonstrated that using ANNs, the optimization of complex chemical systems can be achieved even not knowing or determining the values of the rate constants. Moreover, the solution of differential equations is here not necessary, as well.
- The possibilities of artificial neural networks (ANNs) “soft” computing to evaluate chemical kinetic data have been studied. In the first stage, , a set of “standard” kinetic curves with known parameters (rate constants and/or concentrations of the reactants), which is some kind of “normalized maps”, is prepared. The data base should be built according to a suitable experimental design (ED). In the second stage, such data set is then used for ANNs “learning”. Afterwards, in the second stage, experimental data are evaluated and parameters of “other” kinetic curves are computed without solving anymore the system of differential equations. The combined ED-ANNs approach has been applied to solve several kinetic systems. It was also demonstrated that using ANNs, the optimization of complex chemical systems can be achieved even not knowing or determining the values of the rate constants. Moreover, the solution of differential equations is here not necessary, as well. (en)
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Title
| - Artificial neural networks combined with experimental design: a “soft” approach for chemical kinetics
- Artificial neural networks combined with experimental design: a “soft” approach for chemical kinetics (en)
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skos:prefLabel
| - Artificial neural networks combined with experimental design: a “soft” approach for chemical kinetics
- Artificial neural networks combined with experimental design: a “soft” approach for chemical kinetics (en)
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skos:notation
| - RIV/00216224:14310/12:00059296!RIV13-MSM-14310___
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http://linked.open...avai/predkladatel
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http://linked.open...avai/riv/aktivita
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http://linked.open...avai/riv/aktivity
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http://linked.open...iv/cisloPeriodika
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http://linked.open...vai/riv/dodaniDat
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http://linked.open...aciTvurceVysledku
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http://linked.open.../riv/druhVysledku
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http://linked.open...iv/duvernostUdaju
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http://linked.open...titaPredkladatele
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http://linked.open...dnocenehoVysledku
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http://linked.open...ai/riv/idVysledku
| - RIV/00216224:14310/12:00059296
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http://linked.open...riv/jazykVysledku
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http://linked.open.../riv/klicovaSlova
| - Chemical kinetics; Soft-modelling; Artificial Neural Networks; Experimental design; Rate constants; Multicomponent analysis; Optimization. (en)
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http://linked.open.../riv/klicoveSlovo
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http://linked.open...odStatuVydavatele
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http://linked.open...ontrolniKodProRIV
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http://linked.open...i/riv/nazevZdroje
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http://linked.open...in/vavai/riv/obor
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http://linked.open...ichTvurcuVysledku
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http://linked.open...cetTvurcuVysledku
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http://linked.open...UplatneniVysledku
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http://linked.open...v/svazekPeriodika
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http://linked.open...iv/tvurceVysledku
| - Amato, Filippo
- Havel, Josef
- José Luis, González-Hernández
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http://linked.open...ain/vavai/riv/wos
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http://linked.open...n/vavai/riv/zamer
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issn
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number of pages
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http://bibframe.org/vocab/doi
| - 10.1016/j.talanta.2012.01.044
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http://localhost/t...ganizacniJednotka
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is http://linked.open...avai/riv/vysledek
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