scholarly journals Globally Optimal Parameter Estimates for Non-Linear Diffusions

2008 ◽  
Author(s):  
Aleksandar Mijatovic ◽  
Paul Georg Schneider
Keyword(s):  
Optimal Parameter ◽  
Parameter Estimates ◽  
Non Linear ◽  
Linear Diffusions

Influence of farm, parity, season and litter size on the lactation curve parameters of white British dairy goats

1993 ◽  
Vol 57 (1) ◽  
pp. 99-104 ◽  
Author(s):  
J. C. Williams
Keyword(s):  
Standard Deviation ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Litter Size ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Dairy Goats ◽  
Lactation Curve ◽  
Non Linear ◽  
Research Institute

AbstractThe following goat lactation model was fitted (using non-linear regression) to 407 lactations from five commercial goat dairies and one Research Institute goat herd: y = A exp (B(l + n'/2)n' + Cn' 2 - 1·01/n) where y = daily yield in kg; n = day of lactation (post parturition); and n' = (n -150)1100.Influence of farm, parity and season on the parameter estimates for 376 individual lactations was studied, using multiple linear regression. The models adopted were of the form: A = 1·366 + 1·122 × parity - 0·137 × parity2; ln(-B) = - 1·711 + 0·107 × parity + 0·512 season one; C = 0·037, with a standard deviation for A of 0·658, for ln(-B) of 0·636 and for C of 0·127.Influence of litter size on parameters was investigated for the Research Institute herd. There was no evidence of an effect on any of the model parameters.

Variational Markov chain Monte Carlo for Bayesian smoothing of non-linear diffusions

2011 ◽  
Vol 27 (1) ◽  
pp. 149-176 ◽  
Author(s):  
Yuan Shen ◽  
Dan Cornford ◽  
Manfred Opper ◽  
Cedric Archambeau
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chain Monte Carlo ◽  
Non Linear ◽  
Linear Diffusions ◽  
Bayesian Smoothing

scholarly journals Globally optimal parameter estimates for nonlinear diffusions

2010 ◽  
Vol 38 (1) ◽  
pp. 215-245 ◽  
Author(s):  
Aleksandar Mijatović ◽  
Paul Schneider
Keyword(s):  
Optimal Parameter ◽  
Parameter Estimates ◽  
Nonlinear Diffusions

scholarly journals Optimal parameter estimation for a DC motor using genetic algorithm

2020 ◽  
Vol 11 (2) ◽  
pp. 1047 ◽  
Author(s):  
Mohammad Soleimani Amiri ◽  
Mohd Faisal Ibrahim ◽  
Rizauddin Ramli
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Parameter Estimation ◽  
Numerical Analysis ◽  
Optimal Parameter ◽  
Dc Motor ◽  
Linear Features ◽  
Estimated Parameters ◽  
Optimal Parameter Estimation ◽  
Non Linear

Estimating the parameters of a geared DC motor is crucial in terms of its non-linear features. In this paper, parameters of a geared DC motor are estimated genetically. Mathematical model of the DC motor is determined by Kirchhoff’s law and dynamic model of its shafts and gearbox. Parameters of the geared DC motor are initially estimated by MATLAB/SIMULINK. The estimated parameters are defined as initial values for Genetic Algorithm (GA) to minimize the error of the simulated and actual angular trajectory captured by an encoder. The optimal estimated model of the geared DC motor is validated by different voltages as the input of the actual DC motor and its mathematical model. The results and numerical analysis illustrate it can be ascertained that GA is appropriate to estimate the parameters of platforms with non linear characteristics.

scholarly journals Bayesian calibration of firn densification models

2020 ◽  
Author(s):  
Vincent Verjans ◽  
Amber Alexandra Leeson ◽  
Christopher Nemeth ◽  
C. Max Stevens ◽  
Peter Kuipers Munneke ◽  
...  
Keyword(s):  
Mass Balance ◽  
Optimal Parameter ◽  
Ice Sheet ◽  
Calibration Method ◽  
Parameter Estimates ◽  
Age Difference ◽  
Bayesian Calibration ◽  
Independent Observations ◽  
Ice Sheet Mass Balance ◽  
Parameter Values

Abstract. Firn densification modelling is key to understanding ice sheet mass balance, ice sheet surface elevation change, and the age difference between ice and the air in enclosed air bubbles. This has resulted in the development of many firn models, all relying to a certain degree on parameter calibration against observed data. We present a novel Bayesian calibration method for these parameters, and apply it to three existing firn models. Using an extensive dataset of firn cores from Greenland and Antarctica, we reach optimal parameter estimates applicable to both ice sheets. We then use these to simulate firn density and evaluate against independent observations. Our simulations show a significant decrease (25 and 55 %) in observation-model discrepancy for two models and a small increase (11 %) for the third. As opposed to current methods, the Bayesian framework allows for robust uncertainty analysis related to parameter values. Based on our results, we review some inherent model assumptions and demonstrate how model- and parameter-related uncertainties potentially affect ice sheet mass balance assessments.

scholarly journals Choice of non-linear models to determine the growth curve of meat-type quail

2021 ◽  
Vol 51 (2) ◽  
Author(s):  
Marta Jeidjane Borges Ribeiro ◽  
Fabyano Fonseca Silva ◽  
Maíse dos Santos Macário ◽  
José Aparecido Santos de Jesus ◽  
Claudson Oliveira Brito ◽  
...  
Keyword(s):  
Goodness Of Fit ◽  
Linear Models ◽  
Growth Curves ◽  
Information Criterion ◽  
Metabolizable Energy ◽  
Coefficient Of Determination ◽  
Parameter Estimates ◽  
Coturnix Coturnix ◽  
Non Linear ◽  
Meat Type

ABSTRACT: The objective of this study was to compare non-linear models fitted to the growth curves of quail to determine which model best describes their growth and check the similarity between models by analyzing parameter estimates.Weight and age data of meat-type European quail (Coturnix coturnix coturnix) of three lines were used, from an experiment in a 2 × 4 factorial arrangement in a completely randomized design, consisting of two metabolizable energy levels, four crude protein levels and six replicates. The non-linear Brody, Von Bertalanffy, Richards, Logistic and Gompertz models were used. To choose the best model, the Adjusted Coefficient of Determination, Convergence Rate, Residual Mean Square, Durbin-Watson Test, Akaike Information Criterion and Bayesian Information Criterion were applied as goodness-of-fit indicators. Cluster analysis was performed to check the similarity between models based on the mean parameter estimates. Among the studied models, Richards’ was the most suitable to describe the growth curves. The Logistic and Richards models were considered similar in the analysis with no distinction of lines as well as in the analyses of Lines 1, 2 and 3.

scholarly journals Non-linear Parameter Estimates from Non-stationary MEG Data

2016 ◽  
Vol 10 ◽  
Author(s):  
Juan D. Martínez-Vargas ◽  
Jose D. López ◽  
Adam Baker ◽  
German Castellanos-Dominguez ◽  
Mark W. Woolrich ◽  
...  
Keyword(s):  
Parameter Estimates ◽  
Linear Parameter ◽  
Non Linear

Gradient iterative algorithm for rational models based on Gram-Schmidt orthogonalization method

2021 ◽  
Author(s):  
Yan Pu ◽  
Jing Chen ◽  
Yongqing Yang ◽  
Quanmin Zhu
Keyword(s):  
Iterative Algorithm ◽  
Large Scale ◽  
Optimal Parameter ◽  
Parameter Estimates ◽  
Large Scale Systems ◽  
Rational Models ◽  
Orthogonalization Method ◽  
The Matrix ◽  
Schmidt Orthogonalization ◽  
Information Vector

Abstract An improved gradient iterative algorithm, termed as Gram-Schmidt orthogonalization based gradient iterative algorithm, is proposed for rational models in this paper. The algorithm can obtain the optimal parameter estimates in one iteration for the reason that the information vectors obtained by using the Gram-Schmidt orthogonalization method are independent of each other. Compared to the least squares algorithm and the traditional gradient iterative algorithm, the proposed algorithm does not require the matrix inversion and eigenvalue calculation, thus it can be applied to nonlinear systems with complex structures or large-scale systems. Since the information vector of the rational models contains the latest output that is correlated with the noise, a biased compensation Gram-Schmidt orthogonalization based gradient iterative algorithm is introduced, by which the unbiased parameter estimates can be obtained. Two simulated examples are applied to demonstrate the efficiency of the proposed algorithm.

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