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An Iterative Parameter Estimation Method for Observation Models with Nonlinear Constraints

100%

Dang T.

Metrology and Measurement Systems

2008

tom Vol. 15, nr 4

421-432

This article presents a parameter estimation algorithm for observation models with nonlinear constraints. A prominent example that belongs to this category is the continuous auto-calibration of stereo cameras. Here, our knowledge of the relation between the available measurements and the desired parameters is given by a nonlinear implicit constraint equation. An estimation method derived from an Iterated Extended Kalman Filter is designed for this application. Experiments are conducted with synthetic and real data. The proposed algorithm provides very good results and is readily applicable to a wider range of applications.

Estimating DSGE model parameters in a small open economy: Do real-time data matter?

88%

Capek J.

Review of Economic Perspectives

2015

tom 15

nr 1

89-114

This paper investigates the differences between parameters estimated using real-time and those estimated with revised data. The models used are New Keynesian DSGE models of the Czech, Polish, Hungarian, Swiss, and Swedish small open economies in interaction with the euro area. The paper also offers an analysis of data revisions of GDP growth and inflation and trend revisions of interest rates. Data revisions are found to be unbiased and not autocorrelated in all countries. Inflation is usually measured more accurately in real-time than GDP growth, but this is not the case in the euro area. The results of the core analysis suggest that there are significant differences between parameter estimates using real-time data and those estimated using revised data. The model parameters that are most prone to significant differences between real-time and revised estimations are habit in consumption and persistence of domestic supply, of demand, and of world-wide technology shocks. The impulse response analysis suggests that the model behavior based on real-time and revised data is different.

Robust predictive control of an overhead crane

88%

Smoczek J. , Szpytko J.

2017

tom Vol. 24, No. 2

231--238

The predictive control scheme is developed for an overhead crane using the generalized predictive procedure applied for the discrete time linear parameter-varying model of a crane dynamic. The robust control technique is developed with respect to the constraints of sway angle of a payload and control input signal. The two predictive strategies are presented and compared experimentally. In the first predictive control scheme, the online estimation of the parameters of a crane dynamic model is performed using the recursive least square algorithm. The second approach is a sensorless anti-sway control strategy. The sway angle feedback signal is estimated by a linear parameter-varying model of an unactuated pendulum system with the parameters interpolated using a quasi-linear fuzzy model designed through utilizing the P1-TS fuzzy theory. The fuzzy interpolator is applied to approximate the parameters of a crane discrete-time dynamic model within the range of scheduling variables changes: the rope length and mass of a payload. The experiments carried out on a laboratory scaled overhead crane confirmed effectiveness and feasibility of the proposed solutions. The implementation of control systems was performed using the PAC system with RX3i controller. The series of experiments carried out for different operating points proved robustness of the control approaches presented in the article.

Adaptive methods of moving car detection in monocular image sequences

75%

Kasprzak W.

Machine Graphics and Vision

2000

tom Vol. 9, No. 1/2

167-185

Computer vision applications for traffic scene analysis and autonomous navigation (driver support) require highly sophisticated sensors and computation method- they constitute a real challenge for image analysis systems. Common to bith applications is the moving object detection/tracking task. In this paper we study this task on four different data abstraction levels: image segmentation, 2-D object tracking, model-based 3-D object tracking and many-object traffic scene description. Two meanings of the term "adaptive" are considered: learning algorithm or connectionist systems and recursive estimation for dynamic systems. Generally the firts approach may be appled for low- and segmentation-level analysis of finite image seguences, whereas the second approach for 2-D and 3-D object tracking and estimation.

Recursive identification of noisy autoregressive models via a noise-compensated overdetermined instrumental variable method

63%

Barbieri M. , Diversi R.

International Journal of Applied Mathematics and Computer Science

2024

tom Vol. 34, no. 1

65--79

The aim of this paper is to develop a new recursive identification algorithm for autoregressive (AR) models corrupted by additive white noise. The proposed approach relies on a set of both low-order and high-order Yule-Walker equations and on a modified version of the overdetermined recursive instrumental variable method, leading to the estimation of both the AR coefficients and the additive noise variance. The main motivation behind our proposition is introducing model identification procedures suitable for implementation on edge-computing platforms and programmable logic controllers (PLCs), which are known to have limited capabilities and resources when dealing with complex mathematical computations (i.e., matrix inversion). Indeed, our development is focused on condition monitoring systems, with particular attention paid to their integration onboard industrial machinery. The performance of the recursive approach is tested using both numerical simulations and a laboratory case study. The obtained results are very promising.