The paper presents data-drivenmodelling as process of building a model of a natural process rather than purely machinelearning, and gives an overview of several less popular methods that, however,proved to be accurate and robust: regression and model trees, instance-based learning,committee machines...
For data-driven modeling, data pre-processing is a critical step before construction of the model. The aim of this step is to improve the quality of the data and transform the original data to more appropriate form[20]. For example, the outlier needs to be removed from the modeling dataset...
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Medium- and long-term EPF consider forecast horizons of up to several months (medium-term) or years (long-term), and are mostly obtained using econometric, market-based models, which capture the market dynamics by modelling the marginal cost of electricity generators [[35], [36], [37], [...
Concern is emerging regarding the challenges posed by spatial complexity for modelling and managing the area-wide elimination of parasitic infections. While this has led to calls for applying heterogeneity-based approaches for addressing this complexity,
perspectives. Based on environmental factors and crop growth data, the growth processes of crops and their responses to the environment can be simulated quantitatively. The basic principles of crop growth models are illustrated, and applications of data-driven crop modeling in plant factories are ...
Data-driven modelling can be useful for modelling processes about which adequate knowledge of the physics is limited. In the present paper a sediment transport model based on an artificial neural network (ANN) is presented. The predictive accuracy of the ANN model on the data sets compiled by ...
Our work thus elucidates microscopic mechanism of twitching, and it represents a systematic data-driven approach to quantitative modelling of complex biological phenomena. 展开 关键词: MOTILITY of bacteria PHENOMENOLOGICAL biology BACTERIAL cell surfaces BACTERIAL cell walls BIOLOGICAL models ...
Further, development of data-driven models has the potential to transform neuroengineering applications, including brain-computer interfaces (BCIs) and neuroprostheses to rehabilitate, assist, and augment the human nervous system. Modeling the human brain is particularly challenging in large part because ...
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