scholarly journals MODEL PARAMETER ESTIMATION BY GLOBAL OPTIMIZATION ALGORITHM ACCO COMPLEMENTED BY AN ANN-BASED ERROR ESTIMATOR

2008 ◽  
Vol 52 ◽  
pp. 1411-1416
Author(s):  
Wataru NISHIDA ◽  
Dimitri P SOLOMATINE ◽  
Masato NOGUCHI ◽  
Seiji SUZUKI
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Optimization Algorithm ◽  
Error Estimator ◽  
Global Optimization Algorithm ◽  
Model Parameter ◽  
Model Parameter Estimation

Model parameter estimation of the PEMFCs using improved Barnacles Mating Optimization algorithm

Energy ◽  
2020 ◽  
Vol 212 ◽  
pp. 118738
Author(s):  
Zixuan Yang ◽  
Qian Liu ◽  
Leiyu Zhang ◽  
Jialei Dai ◽  
Navid Razmjooy
Keyword(s):  
Parameter Estimation ◽  
Optimization Algorithm ◽  
Model Parameter ◽  
Model Parameter Estimation

Differential simulated annealing: a robust and efficient global optimization algorithm for parameter estimation of biological networks

2014 ◽  
Vol 10 (6) ◽  
pp. 1385-1392 ◽  
Author(s):  
Ziwei Dai ◽  
Luhua Lai
Keyword(s):  
Global Optimization ◽  
Parameter Estimation ◽  
Simulated Annealing ◽  
Optimization Algorithm ◽  
Biological Networks ◽  
Efficient Global Optimization ◽  
Global Optimization Algorithm ◽  
Large Networks

DSA outperformed five other algorithms in parameter estimation of 95 biological networks and showed significant advantage in large networks.

scholarly journals Optimizing the Estimation of a Histogram-Bin Width—Application to the Multivariate Mixture-Model Estimation

Mathematics ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. 1090 ◽  
Author(s):  
Branislav Panić ◽  
Jernej Klemenc ◽  
Marko Nagode
Keyword(s):  
Parameter Estimation ◽  
Mixture Model ◽  
Optimization Algorithm ◽  
Likelihood Estimation ◽  
Gaussian Mixture ◽  
Difficult Problem ◽  
Integer Optimization ◽  
Model Parameter ◽  
Model Parameter Estimation ◽  
Histogram Estimation

A maximum-likelihood estimation of a multivariate mixture model’s parameters is a difficult problem. One approach is to combine the REBMIX and EM algorithms. However, the REBMIX algorithm requires the use of histogram estimation, which is the most rudimentary approach to an empirical density estimation and has many drawbacks. Nevertheless, because of its simplicity, it is still one of the most commonly used techniques. The main problem is to estimate the optimum histogram-bin width, which is usually set by the number of non-overlapping, regularly spaced bins. For univariate problems it is usually denoted by an integer value; i.e., the number of bins. However, for multivariate problems, in order to obtain a histogram estimation, a regular grid must be formed. Thus, to obtain the optimum histogram estimation, an integer-optimization problem must be solved. The aim is therefore the estimation of optimum histogram binning, alone and in application to the mixture model parameter estimation with the REBMIX&EM strategy. As an estimator, the Knuth rule was used. For the optimization algorithm, a derivative based on the coordinate-descent optimization was composed. These proposals yielded promising results. The optimization algorithm was efficient and the results were accurate. When applied to the multivariate, Gaussian-mixture-model parameter estimation, the results were competitive. All the improvements were implemented in the rebmix R package.

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