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• W2022887651 abstract "► We use ProBMoT, a tool for automated modeling of dynamical systems . ► ProBMoT learns process-based models from measurements and domain knowledge. ► ProBMoT learns both model structure and parameters using different estimation methods. ► We compare global and local parameter estimation in ProBMoT on aquatic ecosystems . ► Global parameter estimation in ProBMoT finds more accurate models on a range of tasks. Modeling dynamical systems involves two subtasks: structure identification and parameter estimation. ProBMoT is a tool for automated modeling of dynamical systems that addresses both tasks simultaneously. It takes into account domain knowledge formalized as templates for components of the process-based models: entities and processes. Taking a conceptual model of the system, the library of domain knowledge, and measurements of a particular dynamical system, it identifies both the structure and numerical parameters of the appropriate process-based model. ProBMoT has two main components corresponding to the two subtasks of modeling. The first component is concerned with generating candidate model structures that adhere to the conceptual model specified as input. The second subsystem uses the measured data to find suitable values for the constant parameters of a given model by using parameter estimation methods. ProBMoT uses model error to rank model structures and select the one that fits measured data best. In this paper, we investigate the influence of the selection of the parameter estimation methods on the structure identification. We consider one local (derivative-based) and one global (meta-heuristic) parameter estimation method. As opposed to other comparative studies of parameter estimation methods that focus on identifying parameters of a single model structure, we compare the parameter estimation methods in the context of repetitive parameter estimation for a number of candidate model structures. The results confirm the superiority of the global optimization methods over the local ones in the context of structure identification." @default.
• W2022887651 created "2016-06-24" @default.
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• W2022887651 date "2012-10-01" @default.
• W2022887651 modified "2023-10-14" @default.
• W2022887651 title "The influence of parameter fitting methods on model structure selection in automated modeling of aquatic ecosystems" @default.
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• W2022887651 doi "https://doi.org/10.1016/j.ecolmodel.2012.06.001" @default.
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