321,813 research outputs found
Simultaneous State and Parameter Estimation of Distributed-Parameter Physical Systems based on Sliced Gaussian Mixture Filter
This paper presents a method for the simultaneous state and parameter estimation of finite-dimensional models of distributed systems monitored by a sensor network. In the first step, the distributed system is spatially and temporally decomposed leading to a linear finite-dimensional model in state space form. The main challenge is that the simultaneous state and parameter estimation of such systems leads to a high-dimensional nonlinear problem. Thanks to the linear substructure contained in the resulting finite-dimensional model, the development of an overall more efficient estimation process is possible. Therefore, in the second step, we propose the application of a novel density representation - sliced Gaussian mixture density - in order to decompose the estimation problem into a (conditionally) linear and a nonlinear problem. The systematic approximation procedure minimizing a certain distance measure allows the derivation of (close to) optimal and deterministic results. The proposed estimation process provides novel prospects in sensor network applications. The performance is demonstrated by means of simulation results
A Manifesto for the Equifinality Thesis.
This essay discusses some of the issues involved in the identification and predictions of hydrological models given some calibration data. The reasons for the incompleteness of traditional calibration methods are discussed. The argument is made that the potential for multiple acceptable models as representations of hydrological and other environmental systems (the equifinality thesis) should be given more serious consideration than hitherto. It proposes some techniques for an extended GLUE methodology to make it more rigorous and outlines some of the research issues still to be resolved
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Three decades of the Shuffled Complex Evolution (SCE-UA) optimization algorithm: Review and applications
Bayesian Optimization with Dimension Scheduling: Application to Biological Systems
Bayesian Optimization (BO) is a data-efficient method for global black-box
optimization of an expensive-to-evaluate fitness function. BO typically assumes
that computation cost of BO is cheap, but experiments are time consuming or
costly. In practice, this allows us to optimize ten or fewer critical
parameters in up to 1,000 experiments. But experiments may be less expensive
than BO methods assume: In some simulation models, we may be able to conduct
multiple thousands of experiments in a few hours, and the computational burden
of BO is no longer negligible compared to experimentation time. To address this
challenge we introduce a new Dimension Scheduling Algorithm (DSA), which
reduces the computational burden of BO for many experiments. The key idea is
that DSA optimizes the fitness function only along a small set of dimensions at
each iteration. This DSA strategy (1) reduces the necessary computation time,
(2) finds good solutions faster than the traditional BO method, and (3) can be
parallelized straightforwardly. We evaluate the DSA in the context of
optimizing parameters of dynamic models of microalgae metabolism and show
faster convergence than traditional BO
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