This paper presents a new robust algorithm for the identification of linear time-invariant systems in the frequency domain. The method is computationally very efficient, and accurately synthesises transfer functions w...
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This paper presents a new robust algorithm for the identification of linear time-invariant systems in the frequency domain. The method is computationally very efficient, and accurately synthesises transfer functions which require a large amount of poles. Some numerical difficulties are avoided by combining the use of a Sanathanan-Koerner iteration and orthonormal rational functions.
The paper deals with a problem of state estimation for nonlinear continuous stochastic systems with discrete-time measurements. A general recursive solution of the estimation problem given by the Bayesian rule and by ...
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The paper deals with a problem of state estimation for nonlinear continuous stochastic systems with discrete-time measurements. A general recursive solution of the estimation problem given by the Bayesian rule and by the Fokker-Planck equation is described. Local estimation methods employing analytical approach to solution and global estimation methods employing analytical, numerical and simulation approaches are discussed. A software package for state estimation of continuous stochastic systems with discrete-time measurements is developed and described. It serves for system design, system simulation, estimator setup and state estimation. The package is designed to embody easily user defined estimators and thus it is suitable for estimator testing and quality comparison of different estimators. Usage of the nonlinear filtering software package is illustrated in a numerical example.
The use of survey plans, which contemplate several tries or call-backs when endeavouring to capture individual data, may supply unarguable information in certain sampling situations with non-ignorable non-response. Th...
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The use of survey plans, which contemplate several tries or call-backs when endeavouring to capture individual data, may supply unarguable information in certain sampling situations with non-ignorable non-response. This paper presents an algorithm whose final aim is the estimation of the individual non-response probabilities from a general perspective of discrete response regression models, which includes the well known probit and logit models. It will be assumed that the respondents supply all the variables of interest when they are captured. Nevertheless, the call-backs continue. even after previous captures, for a small number of tries, r, which has been fixed beforehand only for estimating purposes. The different retries or call-backs are supposed to be carried out with different capture intensities. As mentioned above. the response probabilities, which may vary from one individual to another, are sought by discrete response regression models, whose parameters are estimated from conditioned likelihoods evaluated on the respondents only. The algorithm, quick and easy to implement, may be used even when the capture indicator matrix has been partially recorded. Finally, the practical performance of the proposed procedure is tested and evaluated from empirical simulations whose results are undoubtedly encouraging. (C) 2003 Elsevier B.V. All rights reserved.
This work presents the digitally controlled sensorless drive applied to a drowned pump. The developed drive provides the high function quality and high reliability. The drive architecture from the reliability viewpoin...
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ISBN:
(纸本)0780387384
This work presents the digitally controlled sensorless drive applied to a drowned pump. The developed drive provides the high function quality and high reliability. The drive architecture from the reliability viewpoint is described and analyzed. The new control and estimation algorithms that have been used are briefly presented. The construction of the used voltage source inverter and control is examined. The results of the drive simulation, confirming high dynamic accuracy and in addition simplification of the digital control and reduction of the required controller computing capacity are presented.
A sensor fusion technique is presented and it is shown to achieve good estimates of the position for a 3 degrees-of-freedom industrial robot model. By using an accelerometer the estimate of the tool position accuracy ...
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A sensor fusion technique is presented and it is shown to achieve good estimates of the position for a 3 degrees-of-freedom industrial robot model. By using an accelerometer the estimate of the tool position accuracy can be improved. The computation of the position is formulated as a Bayesian estimation problem and two solutions are proposed. One using the extended Kalman filter and one using the particle filter. Since the aim is to use the positions estimates to improve trajectory tracking with an iterative learning control method, no computational constraints arise. In an extensive simulation study the performance is compared to the Cramer-Rao lower bound. A significant improvement in position accuracy is achieved using the sensor fusion technique.
A priori information given by the complete modelling of the ballistic behavior of a projectile is simplified to give a pertinent reduced evolution model. This model is composed of quasi-static and dynamic models. An e...
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A priori information given by the complete modelling of the ballistic behavior of a projectile is simplified to give a pertinent reduced evolution model. This model is composed of quasi-static and dynamic models. An extended Kalman filter is designed to estimate dynamic part of the 3 attitude angles (roll in [0, 2π], angle of attack and side-slip in the range of few milliradians) from measures of the magnetic field of the earth given by a three-axis magnetometer sensor embedded on the projectile. The algorithm has been tested in simulation, using realistic evolution of attitude data with measurement noise.
Numerical solution of filtering problem for nonlinear stochastic systems is treated. The aim is to improve the point-mass method for multimodal probability density functions of state. The main innovation items concern...
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Numerical solution of filtering problem for nonlinear stochastic systems is treated. The aim is to improve the point-mass method for multimodal probability density functions of state. The main innovation items concern grid update, namely covering a nonnegligible probability density function support and merging grids in multigrid design. Comparing to the standard point-mass algorithm, the new boundary-based grid placement technique maintains estimation quality and the merging technique decreases computational demands for multimodal densities.
In this paper, a new approach to estimate scaling factors of the fuzzy PID controller is presented. The performance of the fuzzy PID controller is sensitive to the variety of scaling factors. The design procedure dwel...
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In this paper, a new approach to estimate scaling factors of the fuzzy PID controller is presented. The performance of the fuzzy PID controller is sensitive to the variety of scaling factors. The design procedure dwells on the use of evolutionary computing (more specifically, a genetic algorithm) and estimation algorithm. The tuning of the scaling factors of the fuzzy PID controller is essential to the entire optimization process. And then we estimate scaling factors of the fuzzy PID controller by means of three types of estimation algorithms such as HCM (Hard C-Means) clustering-based regression polynomial, neuro-fuzzy networks, and regression polynomials. Numerical studies are presented in detail along with a detailed comparative analysis. (C) 2004 Elsevier Ltd. All rights reserved.
In H.264/AVC, tree structured motion estimation enhances the coding efficiency significantly while dramatically increasing the computational complexity of block matching. In the paper, a successive elimination algorit...
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In H.264/AVC, tree structured motion estimation enhances the coding efficiency significantly while dramatically increasing the computational complexity of block matching. In the paper, a successive elimination algorithm (SEA) is implemented in tree structured motion estimation with a simple and effective method to determine the initial motion vector, which exploits the strong correlation among the partially overlapped variable-size blocks. With identical performance to a full search algorithm, computations. for block matching can be reduced to 1%-20%. Further, the SEA can be improved by incorporating two early termination conditions, then named 'Quick SEA'. Finally, a novel fast motion estimation algorithm, successive elimination diamond search (SEDS), is proposed by efficiently integrating the Quick SEA and a modified diamond search pattern. Simulation results show that the proposed Quick SEA can reduce the computational complexity of block matching by 3-5 times compared to the basic SEA. SEDS further reduces by about one-half the computations of Quick SEA. With similar rate distortion performance, 0.2%-1% block matching distortion is calculated for SEDS with corresponding speed-up factors of 100 to 500 in comparison with the full search algorithm.
Simulation of complex (bio)chemical reactions plays an important role in a process kinetics characterisation. However, detailed kinetic modelling is a difficult task because the model has to reflect the process comple...
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Simulation of complex (bio)chemical reactions plays an important role in a process kinetics characterisation. However, detailed kinetic modelling is a difficult task because the model has to reflect the process complexity under variate operating conditions, starting from a limited number of observed variables, (non-)conventional data recorded with a limited sampling frequency, and often with a low reproducibility. Extensive investigations can lead to structured models of complexity depending on the utilisation scope. To overcome weak results, the identification problem must be well formulated, data consistent, numerical estimation appropriate and effective, and the estimate quality analysis adequate. While statistical estimation theory has been extensively developed in terms of objective function choice and solution analysis, numerical algorithm application to (bio)chemical kinetic systems presents particularities and difficulties. The present paper aims to review the main steps and trends in solving the kinetic model identification problem. Rules for a suitable problem formulation vs. modelling objectives, advanced numerical algorithms for obtaining a reliable solution, and an estimate of suitable analysis are shortly summarised.
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