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- W2405322643 abstract "Symbolic regression is an application of genetic programming and is used for modeling different dynamic processes. Industrial processes problems have been solved using this technique. In this work a symbolic regression algorithm is used for modeling the synthesis process of the oxides Bi <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> MoO <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>6</sub> and V <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sub> O <sub xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>5</sub> in order to provide a model. These oxides are used on heterogeneous photo catalysis. Genetic programming, artificial neural network and linear regression are compared with symbolic regression models using statistics metrics to evaluate them." @default.
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- W2405322643 date "2014-11-01" @default.
- W2405322643 modified "2023-09-27" @default.
- W2405322643 title "Modeling Synthesis Processes of Photocatalysts Using Symbolic Regression &#x03B1;-&#x03B2;" @default.
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- W2405322643 doi "https://doi.org/10.1109/micai.2014.33" @default.
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