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- W2115449848 abstract "In this article we present the classification of batch fermentation models. A recurrent neural network uses temporal information on the state variables together with the time values. It can select from several possible models of the process the model that best describes the dynamics of the process. A pre-treatment of the data, denoted by 'selfnormalization' is also proposed. It is shown by a parameter sensitivity study that the 'self-normalization' assigns to a family of models (same structure of model, with different parameters) an approximately unique representation. This representation is used for training the recurrent neural network. The dimension of the learning set is considerably reduced. The trained neural network is used for the classification of real lactic fermentation data. The model which best suits the experimental data is determined and, from this, the main phenomena governing the process. The response of the neural classifier represents only a comparative measure of belonging to each of t..." @default.
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- W2115449848 date "2001-01-01" @default.
- W2115449848 modified "2023-09-24" @default.
- W2115449848 title "Classification of fermentation process models using recurrent neural networks" @default.
- W2115449848 doi "https://doi.org/10.1080/00207720010017607" @default.
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