Observability of state variables and parameters of a dynamical system from an observed time series is analyzed and quantified by means of the Jacobian matrix of the delay coordinates map. For each state variable and each parameter to be estimated, a measure of uncertainty is introduced depending on the current state and parameter values, which allows us to identify regions in state and parameter space where the specific unknown quantity can(not) be estimated from a given time series. The method is demonstrated using the Ikeda map and the Hindmarsh-Rose model
Mathematical models in form of dynamical systems play an important role in many disciplines, such ...
International audienceThe objective of this study is the analysis of dynamic systems represented by ...
State-space models have been increasingly used to study macroeconomic and financial problems. A stat...
Observability of state variables and parameters of a dynamical system from an observed time series i...
Features of the Jacobian matrix of the delay coordinates map are exploited for quantifying the robus...
Parameter estimation is a vital component of model development. Making use of data, one aims to dete...
An observability problem for a class of linear, uncertain-parameter, time-invariant dynamic SISO sys...
We present two methods to estimate bounds of parameter uncertainty in state-space systems. In the fi...
In most applications in control engineering a measurement of all state variables is either impossibl...
In data-driven system identification, values of parameters and not observed variables of a given mod...
Transferring information from observations to models of complex systems may meet impediments when th...
The problem of estimating bounds for timevarying parameter perturbations using measurement data is a...
The estimation of unmeasured state and parameters for complex systems is of great importance for app...
Obtaining accurate models that can predict the behaviour of dynamic systems is important for a varie...
In this work, two novel dynamics indicators are introduced and used to characterise the uncertain dy...
Mathematical models in form of dynamical systems play an important role in many disciplines, such ...
International audienceThe objective of this study is the analysis of dynamic systems represented by ...
State-space models have been increasingly used to study macroeconomic and financial problems. A stat...
Observability of state variables and parameters of a dynamical system from an observed time series i...
Features of the Jacobian matrix of the delay coordinates map are exploited for quantifying the robus...
Parameter estimation is a vital component of model development. Making use of data, one aims to dete...
An observability problem for a class of linear, uncertain-parameter, time-invariant dynamic SISO sys...
We present two methods to estimate bounds of parameter uncertainty in state-space systems. In the fi...
In most applications in control engineering a measurement of all state variables is either impossibl...
In data-driven system identification, values of parameters and not observed variables of a given mod...
Transferring information from observations to models of complex systems may meet impediments when th...
The problem of estimating bounds for timevarying parameter perturbations using measurement data is a...
The estimation of unmeasured state and parameters for complex systems is of great importance for app...
Obtaining accurate models that can predict the behaviour of dynamic systems is important for a varie...
In this work, two novel dynamics indicators are introduced and used to characterise the uncertain dy...
Mathematical models in form of dynamical systems play an important role in many disciplines, such ...
International audienceThe objective of this study is the analysis of dynamic systems represented by ...
State-space models have been increasingly used to study macroeconomic and financial problems. A stat...