期刊
JOURNAL OF THE ROYAL SOCIETY INTERFACE
卷 16, 期 156, 页码 -出版社
ROYAL SOC
DOI: 10.1098/rsif.2019.0043
关键词
identifiability; system identification; parameter estimation; observability; input reconstruction; dynamic modelling
资金
- European Union's Horizon 2020 research and innovation programme [675585]
- Spanish Ministry of Science, Innovation and Universities, project SYNBIOCONTROL [DPI2017-82896-C2-2-R]
In this paper, we address the system identification problem in the context of biological modelling. We present and demonstrate a methodology for (i) assessing the possibility of inferring the unknown quantities in a dynamic model and (ii) effectively estimating them from output data. We introduce the term Full Input-State-Parameter Observability (FISPO) analysis to refer to the simultaneous assessment of state, input and parameter observability (note that parameter observability is also known as identifiability). This type of analysis has often remained elusive in the presence of unmeasured inputs. The method proposed in this paper can be applied to a general class of nonlinear ordinary differential equations models. We apply this approach to three models from the recent literature. First, we determine whether it is theoretically possible to infer the states, parameters and inputs, taking only the model equations into account. When this analysis detects deficiencies, we reformulate the model to make it fully observable. Then we move to numerical scenarios and apply an optimization-based technique to estimate the states, parameters and inputs. The results demonstrate the feasibility of an integrated strategy for (i) analysing the theoretical possibility of determining the states, parameters and inputs to a system and (ii) solving the practical problem of actually estimating their values.
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