The next section is dedicated to briefly describe the data selection techniques, the model parameterization of GRIMM-2, and the model estimation techniques.
A creative mixture of both simple and advanced time series models, estimation techniques and expert knowledge can solve problems caused by the limited availability of historical population forecasts and a general lack of reliable, internationally comparable data series on international migration.
Through combining the RKF with the interacting multiple model (IMM) estimation technique, a RKF/IMM algorithm is proposed for such JMSS.
We illustrate the numerical implementation of our model and estimation technique using a 4-year Toronto DATs data set compiled by the National Climatic Data Center.
Massey et al. (2014) evaluated the performance of the model-assisted estimation technique in connection with the Swiss national forest inventory.
With this model and parameter estimation technique, electrochemical model-based state of charge (SOC) estimation can be realized online and can provide accurate results.
In order to verify the modeling and estimation techniques, a micro-vibration model of a numerical RWA is established and its estimation error is validated.
This paper presents: various model transformations, an estimation technique for the steering cost parameters, and three case studies that show how our model transformations result in factors improvement in memory footprint and performance with respect to the initial implementation.
We used model-based estimation techniques for parameters underlying this physical model.
