scholarly journals Optimal model for weaning-weight of Bunaji Bulls at NAPRI Farm, Shika, Nigeria

2021 ◽  
Vol 8 (2) ◽  
pp. 1199-1210
Author(s):  
Anasu Rabe
Keyword(s):  
Input Data ◽  
Optimal Parameter ◽  
Empirical Models ◽  
Parameter Estimates ◽  
Covariance Model ◽  
Optimal Model ◽  
Major Drawback ◽  
Weaning Weight ◽  
Production Research ◽  
Mature Weight

Empirical models have over the years been commonly established by animal research centers for the study of weight-age profiles in order to understand the metabolic processes of growth. They provide efficient parameter estimates for mature weight and rate of maturing, but were found to consistently either over-or-under estimate the mature weight. estimate the mature weight. They also perform poorly in predicting weight in early life or beyond the range of input data. At the National Animal Production Research Institute (NAPRI) farm, Shika, Brody was established as the model that provides efficient parameter estimates of weight-age profiles for Bunaji bulls. However, a major drawback of the model is its consistent underestimation of weight prior to six months of age, leading to poor prediction of weaning weight. To address this shortcoming, we propose in this article a joint mean-covariance model that provide optimal parameter estimates for the weaning weight of Bunaji bulls

scholarly journals Robust estimation of hydrological model parameters

2008 ◽  
Vol 5 (3) ◽  
pp. 1641-1675 ◽  
Author(s):  
A. Bárdossy ◽  
S. K. Singh
Keyword(s):  
Measurement Errors ◽  
Input Data ◽  
Hydrological Model ◽  
Optimal Parameter ◽  
Model Performance ◽  
Model Parameters ◽  
Geometrical Approach ◽  
State Variables ◽  
Parameter Vector ◽  
Robust Parameter

Abstract. The estimation of hydrological model parameters is a challenging task. With increasing capacity of computational power several complex optimization algorithms have emerged, but none of the algorithms gives an unique and very best parameter vector. The parameters of hydrological models depend upon the input data. The quality of input data cannot be assured as there may be measurement errors for both input and state variables. In this study a methodology has been developed to find a set of robust parameter vectors for a hydrological model. To see the effect of observational error on parameters, stochastically generated synthetic measurement errors were applied to observed discharge and temperature data. With this modified data, the model was calibrated and the effect of measurement errors on parameters was analysed. It was found that the measurement errors have a significant effect on the best performing parameter vector. The erroneous data led to very different optimal parameter vectors. To overcome this problem and to find a set of robust parameter vectors, a geometrical approach based on the half space depth was used. The depth of the set of N randomly generated parameters was calculated with respect to the set with the best model performance (Nash-Sutclife efficiency was used for this study) for each parameter vector. Based on the depth of parameter vectors, one can find a set of robust parameter vectors. The results show that the parameters chosen according to the above criteria have low sensitivity and perform well when transfered to a different time period. The method is demonstrated on the upper Neckar catchment in Germany. The conceptual HBV model was used for this study.

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

PSI-17 Genetic analysis of weaning weight in Hungarian Simmental cattle

2021 ◽  
Vol 99 (Supplement_3) ◽  
pp. 274-275
Author(s):  
Afees Ajasa ◽  
Barnabás Vágó ◽  
Imre Füller ◽  
István Komlósi ◽  
János Posta
Keyword(s):  
Variance Components ◽  
Random Effect ◽  
Random Effect Model ◽  
Genetic Effect ◽  
Genetic Effects ◽  
Covariance Model ◽  
Genetic Covariance ◽  
Weaning Weight ◽  
Effect Model ◽  
Maternal Heritability

Abstract The aim of the study was to partition the total phenotypic variation in the weaning weight of Hungarian Simmental calves into their various causal components. The data used was provided by the Association of Hungarian Simmental Breeders. The dataset comprised of the weaning weight records of 44,278 calves (sire = 879, dam = 14,811) born from 1975 to 2020. A total of six models were fitted to the weaning weight data. Herd, birth year, calving order and sex were included as fixed effects in the models. Model 1 had direct genetic effect as the only random effect. Model 2 had a permanent maternal environment as an additional random effect. Model 3 had both direct and maternal genetic effects, with their covariance is being zero. Model 4 was similar to Model 3 but with non-zero direct-maternal genetic covariance. Model 5 had direct, maternal genetic and permanent environmental effects and a zero direct-maternal genetic covariance. Model 6 was similar to model 5 but the direct-maternal genetic effect was assumed to be correlated. Variance components and genetic parameters were estimated using restricted maximum likelihood method with the Wombat software. The best fit model was determined using the Log likelihood ratio test. Inclusion of direct maternal genetic covariance increased the variance components estimates dramatically which resulted in a corresponding increase in the direct and maternal heritability estimates. The best fitted model (Model 4) had direct and maternal genetic effects as the only random effects with a non-zero direct-maternal genetic covariance. The direct heritability, maternal heritability and direct-maternal genetic correlation estimate of the best model was 0.57, 0.16 and -0.78, respectively. Our result suggests the problem of (co)sampling variation in the partitioning of additive genetic effect into direct and maternal components.

Optimal Model Selection of Suport Vector Classifiers for Rolling Element Bearings Fault Detection Using Statistical Time-Domain Features

2008 ◽  
Author(s):  
Z. Hameed ◽  
Y. S. Hong ◽  
Y. M. Cho ◽  
S. H. Ahn ◽  
C. K. Song
Keyword(s):  
Machine Learning ◽  
Model Selection ◽  
Time Domain ◽  
Input Data ◽  
Good Choice ◽  
Abnormal Behavior ◽  
Support Vector ◽  
Optimal Model ◽  
Super Computer ◽  
Statistical Time

Support Vector Machines (SVMs) are being used extensively now days in the arena of pattern recognition and regression analysis. It has become a good choice for machine learning both for supervised and unsupervised learning purposes. The SVM is primarily based on the mapping the data to a hyperplane using some kernel function and then increasing the margin between the hype planes so this hyperplane classifies the data in the normal and fault state. Due to large amount of input data, it is computationally cumbersome to yield the desired results in shortest possible time by using SVM. To overcome this difficulty in this work, we have employed statistical Time-Domain Features like Root Mean Square (RMS), Variance, Skewness and Kurtosis as pre-processors to the input raw data. Then various combinations of these time-domains signals and features have been used as inputs and their effects on the optimal model selection have been investigated thoroughly and optimal one has been suggested. The procedure presented here is computational less expensive otherwise to process the input data for model selection we may have to use super computer. The implementation of proposed method for machine learning is not much complicated and by using this procedure, an impending fault/abnormal behavior of the machine can be detected beforehand.

scholarly journals A modeling approach to ultrasound evaluation of material properties

2010 ◽  
Author(s):  
◽  
Sri Waluyo
Keyword(s):  
Ultrasonic Waves ◽  
Binary Sequences ◽  
Model Verification ◽  
Parameter Estimates ◽  
Model Parameters ◽  
Optimal Model ◽  
Ultrasound Evaluation ◽  
Voigt Model ◽  
Estimation Of Model Parameters ◽  
Linear State

A method for estimating the mechanical properties of a viscoelastic sample from ultrasound measurements was developed. The sample was represented as a mechanical network according to the Kelvin-Voigt model and linear state-space equations were derived to describe the system dynamics. Four parameters can be extracted by comparing the model with measured transmission waves. These parameters can be related to viscoelastic properties of the sample. Broadband pseudo-random binary sequences were designed and used to perturb the sample. The Levenberg-Marquardt method was employed to adjust the model parameters and the least-squares algorithm was used to obtain optimal model parameter estimates. Model verification showed that the algorithm developed could converge to known model parameters. Estimated model parameters showed consistency and reflected known facts about the materials tested. The model could capture the major dynamics of transmitted ultrasonic waves and allow repeatable estimation of model parameters. The model parameters could not only differentiate the materials tested but also follow expected trends of variation. The model parameters were useful for sensory crispness prediction and crispness was more correlated to the elastic modulus than to viscosity, which is consistent with existing research.

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.

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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