Nonlinear parameter estimation

By Y. Bard. 1974

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

Citations in the corpus

Causal Models for Dynamical Systems (2020, background): “This includes various versions of goodness-of-fit of the integrated system, nonlinear least squares methods, or gradient matching [Bard 1974, Benson 1979, Varah 1982, Ramsay et al. 2007, Calderhead et al. 2009, Oates et al. 2014, Dattner and Klaassen 2015, Macdonald and Husmeier 2015, Raue et al. 20”
State of charge estimation using an unscented filter for high power lithium ion cells (2010, methodology): “The conventional estimation protocols invoke a minimization technique such as the Nelder–Mead algorithm or the Levenberg–Marquardt algorithm [42,43], which is a complex iterative process.”
Reliable nonlinear parameter estimation using interval analysis: error-in-variable approach (2000, methodology): “The mathematical modeling of physical phenomena is a core aspect of the simulation and optimization tools used for purposes of chemical process design and control.”
T‐optimum designs for discrimination between two multiresponse dynamic models (2005, background): “Parameter estimation methods for such chemical kinetic models are relatively well developed (Bard, 1974; Seber and Wild, 1989; Schittkowski, 2002).”
Gaussian Processes for history-matching: application to an unconventional gas reservoir (2017, methodology): “Gradient-based optimization techniques such as GaussNewton [9, 10] may not be very useful for many nonlinear complex problems.”
The glass transition temperature versus the fictive temperature (2007, background): “the standard error of the fit parameters [53,54].”
Characterization and identification of aquifer heterogeneity with generalized parameterization and Bayesian estimation (2004, methodology): “The fitness function for the GA in this study is the estimated standard deviation of observation errors [Bard, 1974],”
Positron Emission Tomography (2020, unknown): “(b) estimation of trues method (ETM) [42]), (c) triple energy window (TEW) method [45] and (d) multispectral method (indicating energy ranges of first 3”
Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling (2014, methodology): “There exist several classes of parameter estimation meth-ods for ODE models, which include the nonlinear least-square (NLS) method (Hemker 1972; Bard 1974; Li, Osborne, and Pravan 2005; Xue, Miao, and Wu 2010), the two-stage smoothing-based estimation method (Varah 1982; Brunel 2008; Chen and Wu…”
Application of unconstrained optimization and sensitivity analysis to calibration of a soil constitutive model (2003, background): “For instance, a shorter principal axis of the ellipsoid implies that the objective function is more sensitive to the combination of parameters in that direction, and vice versa [22].”
Parameter estimation for basin-scale ecosystem-linked population models of large pelagic predators: Application to skipjack tuna (2008, methodology): “, C = H 1 (Bard, 1974), where H 1⁄4 o2L ohiohj ; i; j 1⁄4 1;2; .”
Calibration‐constrained Monte Carlo analysis of highly parameterized models using subspace techniques (2009, methodology): “Other objective functions exist; however the L2 norm has desirable qualities including the ability to assess parameter and predictive error [Bard, 1974].”
Parsimonious Bayesian Markov chain Monte Carlo inversion in a nonlinear geophysical problem (2002, methodology): “An approximate posterior covariance matrix for ρ can be obtained from a linearization about the point at ρ̃′ (Bard 1974) as Ĉρ ≈ [ JT CeJ + Cρ ]−1 , (B3) where J is the Jacobian matrix (the matrix of derivatives of the predicted data with respect to each element of ρ) evaluated at ρ̃′.”