Using an Ensemble Kalman Filter Method to Calibrate Parameters and Update Soluble Chemical Transfer From Soil to Surface Runoff

2011 ◽  
Vol 91 (1) ◽  
pp. 133-152 ◽  
Author(s):  
Ju-Xiu Tong ◽  
Bill X. Hu ◽  
Jin-Zhong Yang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Surface Runoff ◽  
Filter Method ◽  
Chemical Transfer ◽  
Kalman Filter Method

scholarly journals Solution Estimation of Logistic Growth Model with Ensemble Kalman Filter Method

2014 ◽  
Vol 14 (2) ◽  
pp. 85
Author(s):  
Vianda Nuning Fitriani ◽  
Kosala Dwidja Purnomo
Keyword(s):  
Kalman Filter ◽  
Analytical Solution ◽  
Ensemble Kalman Filter ◽  
Nonlinear Models ◽  
Specific Form ◽  
Filter Method ◽  
Logistic Growth ◽  
Parabolic Form ◽  
Logistic Growth Model ◽  
Kalman Filter Method

Ensemble Kalman Filter (EnKF) can be applied for linear or nonlinear models. This paper is aimed to estimate the logistic growth of population models using EnKF. The estimation will be compared with the analytical solution. We assume that we can find the analytical solution of the models. The models is in the specific form i.e comparison between the population growth rate and the amount of population is in the parabolic form. The good estimation will be attained by choosing 100 as size of ensembles in EnKF. The result of estimation really so closed to the analytical solution. Keywords : Analytical solution, EnKF, ensemble

Real-time forecasting of fire in a two-story building using ensemble Kalman filter method

2018 ◽  
Vol 97 ◽  
pp. 19-28 ◽  
Author(s):  
Jie Ji ◽  
Chunxiang Liu ◽  
Zihe Gao ◽  
Liangzhu (Leon) Wang
Keyword(s):  
Kalman Filter ◽  
Real Time ◽  
Ensemble Kalman Filter ◽  
Filter Method ◽  
Kalman Filter Method ◽  
Story Building

scholarly journals Prediksi Harga Saham pada PT. ABCD menggunakan Ensemble Kalman Filter

2018 ◽  
Vol 4 (1) ◽  
pp. 24-27
Author(s):  
Puspandam Katias ◽  
Denis Fidita ◽  
Teguh - Herlambang
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Stock Exchange ◽  
Estimation Method ◽  
Optimal Investment ◽  
Share Price ◽  
Filter Method ◽  
Price Estimation ◽  
Future Return ◽  
Kalman Filter Method

Stock Exchange is established as an effort to link both stock / security sellers and buyers. Securities often traded in stock market is share. The intention of an investor in investment is to have the lowest risk and to gain the highest profit. To make decision for optimal investment, calculation on the estimate of future return to be gained is necessarily made. One of estimate calculation methods considered the most objective is by applying  the Ensemble Kalman filter (EnKF) method. Ensemble Kalman filter is a method of estimation of condition variable of discrete linear dynamic system that minimizes covarian error of estimation. So, this study aims to apply share price estimation method for close prices of share of PT. ABCD by Ensemble Kalman Filter method as investor' consideration in investment with an error of  3% - 5%.

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