[PDF] Top 20 Data Assimilation Methods for Neuronal State and Parameter Estimation
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Data Assimilation Methods for Neuronal State and Parameter Estimation
... provide parameter bounds in the 4D-Var framework, whereas this is not the case for the ...series data), the optimization may take a computing time scale of days to yield reasonable ... See full document
38
Application of ensemble transform data assimilation methods for parameter estimation in reservoir modeling
... years data assimilation methods have been developed to obtain estimations of uncertain model parameters by taking into account a few observations of a model ...(MCMC) methods are ... See full document
16
State and parameter estimation of two land surface models using the ensemble Kalman filter and the particle filter
... and state variables to simulate the water and en- ergy balance at the soil–atmosphere ...moisture state of the ...different data assimilation methods for joint parameter and ... See full document
32
Limiting the parameter space in the Carbon Cycle Data Assimilation System (CCDAS)
... Many parameter estimation methods, such as gradient- based, Kalman filter, Monte Carlo inversion, Levenberg– Marquardt and genetic algorithm, use the Bayesian approach (Tarantola, 1987, 2005), which ... See full document
11
Biological data assimilation for parameter estimation of a phytoplankton functional type model for the western North Pacific
... In previous studies using LTL marine ecosystem models, various approaches for data assimilation were introduced as methods of estimating optimal physiological parameters (e.g. Kuroda and Kishi, 2004; ... See full document
16
Novel Methods based on the Fusion of Multisensor Remote Sensing Data for Accurate Forest Parameter Estimation
... classification methods are usually employed. The main drawback of these methods is the need of a sufficient number of labeled ground reference samples for train- ing the classification ...sensing ... See full document
163
Accurate state estimation from uncertain data and models: an application of data assimilation to mathematical models of human brain tumors
... on state estimation procedures that are based on the Kalman filter, described in Section ...elements. Methods to reduce the dimensionality of the estimation problem therefore are ... See full document
20
Markov chain Monte Carlo methods for state space models with point process observations
... for parameter estimation and state ...complete data likelihood (also known as the Q-function) was unimodal and highly nongaussian (skewed) with respect to its param- eters (Yuan & ... See full document
26
Improved variational methods in statistical data assimilation
... good estimation of observed state variables (a “good fit”) is not sufficient to produce confi- dence in the quality of the model; good prediction is ... See full document
9
Overcoming Diminished Motivation
... a state that is different from that of the real system leading to inaccurate ...current state of the real system dynamically. The sensor data provides an observation of the system, and data ... See full document
132
Evaluation of a cosmic ray neutron sensor network for improved land surface model prediction
... joint state–parameter estimation in data assimilation systems and showed that the particle filter is an interesting alternative for soil hydraulic parameter estimation for ... See full document
22
Interdisciplinary design methodology for systems of mechatronic systems focus on highly dynamic environmental applications
... vehicle state estimation. State-of-the-art vehicle state estimators rely on methods, which exploit only a limited set of measurements [12] and/or relatively simple models [13, ...main ... See full document
7
Simultaneous state–parameter estimation of rainfall-induced landslide displacement using data assimilation
... Five continuous GPS observation stations have been set up for the Xishan Village landslide to obtain any deformation observations. The GPS receivers were connected to a net- work so the observations could be transferred ... See full document
12
Sensitivity analysis with respect to observations in variational data assimilation for parameter estimation
... initial state of the system under observa- tion is known, and that it is only model parameters (boundary conditions, forcing terms, distributed coefficients, ...the assimilation is in- tended to determine ... See full document
11
Joint state-parameter estimation of a nonlinear stochastic energy balance model from sparse noisy data
... with data coming from the interpolation of proxy data can be used in the modeling framework we have considered, along with the assumption of Gaussian observation ...the parameter estimation ... See full document
24
Diectromagnetic interrogation techniques for damage detection
... experimental data. The data is organized in an optimal way allowing one to use a reduced number of basis elements, resulting in a fast algorithm while still obtaining an accurate approximation to the ... See full document
11
Identification of Recurrent Neural Networks by Bayesian Interrogation Techniques
... unknown parameter θ, then one possible direction is the minimization of, for example, the entropy or the standard deviation of the posterior ...of parameter θ (c T θ). There exist a number of other ... See full document
40
A canonical space-time state space model: state and parameter estimation
... In a maximum likelihood framework, the natural solution to such a problem is to use the well-known expectation-max- imization (EM) algorithm. The application of the EM algorithm to linear dynamic systems [13] has ... See full document
10
An investigation into the properties of Bayesian forecasting models
... A number the of and other single state of on line variance and tested on estimation methods are proposed The methods are shown to be robust artificial and real data.. and the lead to imp[r] ... See full document
461
Multi-timescale data assimilation for atmosphere–ocean state estimates
... Moreover, in the offline case one may use hundreds to thou- sands of ensemble members from multiple models and sim- ulations, reducing the potential for model bias and sampling error. It is also advantageous to use an ... See full document
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