Parameter identification for stochastic hybrid models of biological interaction networks
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A variance analysis of the parameters of a plant, belonging to a class of multivariable systems, estimated in closed-loop operation is performed. More specifically, having in mind the control applications where it is not desirable to excite all external re ...
This paper presents a novel distributed estimation algorithm based on the concept of moving horizon estimation. Under weak observability conditions we prove convergence of the state estimates computed by any sensor to the correct state even when constraint ...
Joint processing of sensor array outputs improves the performance of parameter estimation and hypothesis testing problems beyond the slim of the individual sensor processing results. When the sensors have high data sampling rates, arrays are tethered, crea ...
In this paper we consider (hierarchical, Lagrange) reduced basis approximation and a posteriori error estimation for linear functional outputs of affinely parametrized elliptic coercive partial differential equations. The essential ingredients are (primal- ...
The objective of this study is to investigate the modelling of porosity and hydraulic conductivity changes in saturated porous media induced by the biological growth in pore space. Three different relationships between porosity changes and hydraulic conduc ...
This paper presents methods for the parameter identification of a model of subtilin production by Bacillus subtilis. Based on a stochastic hybrid model, identification is split in two subproblems: estimation of the genetic network regulating subtilin produ ...
The sensitivity of the unmeasured state variables to the measurements strongly affects the rate of convergence of a state estimation algorithm. To overcome potential observability problems, the approach has been to identify the model parameters so as to re ...
A fine term structure models have gained significant attention in the finance literature, mainly due to their analytical tractability and statistical flexibility. The aim of this article is to present both theoretical foundations as well as empirical aspec ...
Compressed sensing (CS) suggests that a signal, sparse in some basis, can be recovered from a small number of random projections. In this paper, we apply the CS theory on sparse background-subtracted silhouettes and show the usefulness of such an approach ...
During the last decades, reduced basis (RB) methods have been developed to a wide methodology for model reduction of problems that are governed by parametrized partial differential equations (PPDEs). In particular equations of elliptic and parabolic type f ...