317,754 research outputs found

    An identification approach to dynamic errors-in-variables systems with a preliminary clustering of observations

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    Errors-in-variables models are statistical models in which not only dependent but also independent variables are observed with error, i.e. they exhibit a symmetrical model structure in terms of noise. The application field for these models is diverse including computer vision, image reconstruction, speech and audio processing, signal processing, modal and spectral analysis, system identification, econometrics and time series analysis. This paper explores applying the errors-in-variables approach to parameter estimation of discrete-time dynamic linear systems. In particular, a framework is introduced in which a preliminary separation step is applied to group observations prior to parameter estimation. As a result, instead of one, two sets of estimates are derived simultaneously, comparing which can yield estimates for noise parameters. The proposed approach is compared to other schemes with simulation examples

    Analysis of an Ultra-precision Positioning System and Parametrization of Its Structural Model for Error Compensation

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    Conventional compensation of position errors of machine tools relies only on measured values. Due to this principle it is not always possible to compensate the errors in time, especially dynamic ones. Moreover, the relevant control variables cannot always be measured directly. Thus, this approach proves to be insufficient for high precision applications. In this context, a model-based error prediction allows for minimal position errors. However, ultra-precision applications set high demands for the models' accuracy. This paper presents the design of an accurate and real time-capable structural model of an ultra-precision positioning system. The modeling method for the developed ultra-precision demonstrator is shown and the initial parameter identification is presented. © 2017 The Authors. Published by Elsevier B.V.DFG/FOR/184

    Structural Multi-Equation Macroeconomic Models: Identification-Robust Estimation and Fit

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    Weak identification is likely to be prevalent in multi-equation macroeconomic models such as in dynamic stochastic general equilibrium setups. Identification difficulties cause the breakdown of standard asymptotic procedures, making inference unreliable. While the extensive econometric literature now includes a number of identification-robust methods that are valid regardless of the identification status of models, these are mostly limited-information-based approaches, and applications have accordingly been made on single-equation models such as the New Keynesian Phillips Curve. In this paper, we develop a set of identification-robust econometric tools that, regardless of the model's identification status, are useful for estimating and assessing the fit of a system of structural equations. In particular, we propose a vector auto-regression (VAR) based estimation and testing procedure that relies on inverting identification-robust multivariate statistics. The procedure is valid in the presence of endogeneity, structural constraints, identification difficulties, or any combination of these, and also provides summary measures of fit. Furthermore, it has the additional desirable features that it is robust to missing instruments, errors-in-variables, the specification of the data generating process, and the presence of contemporaneous correlation in the disturbances. We apply our methodology, using U.S. data, to the standard New Keynesian model such as the one studied in Clarida, Gali, and Gertler (1999). We find that, despite the presence of identification difficulties, our proposed method is able to shed some light on the fit of the considered model and, particularly, on the nature of the NKPC. Notably our results show that (i) confidence intervals obtained using our system-based approach are generally tighter than their single-equation counterparts, and thus are more informative, (ii) most model coefficients are significant at conventional levels, and (iii) the NKPC is preponderantly forward-looking, though not purely so.Inflation and prices; Econometric and statistical methods

    Identification of dynamic errors-in-variables bilinear systems of fractional order

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    An approach for the identification of dynamic single-input single-output bilinear discrete-time fractional order system models within the errors-in-variables framework for the case of white input and output noise sequences is presented. A criterion is obtained that allows obtaining highly consistent estimates of the parameters of the system.The authors gratefully acknowledge the contributions of prof. O.A. Katsyuba in improving the article

    Identification of dynamic errors-in-variables bilinear systems of fractional order

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    An approach for the identification of dynamic single-input single-output bilinear discrete-time fractional order system models within the errors-in-variables framework for the case of white input and output noise sequences is presented. A criterion is obtained that allows obtaining highly consistent estimates of the parameters of the system.The authors gratefully acknowledge the contributions of prof. O.A. Katsyuba in improving the article

    Robusztus becslési és irányítási algoritmusok = Robust identification and control

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    Új algoritmusokat dolgoztunk ki errors-in-variables modellek identifikációjára. A módszerek lehetővé teszik a folyamat és zaj paraméterek együttes becslését. Az algoritmusok a jeltér PCA illetve SVD szeparációjával, majd pedig a szeparált adatbázison végzett paraméterbecsléssel és ezek összevetésével képesek az együttes folyamat és zaj modell meghatározására. Továbbá módszereket dolgoztunk ki célfüggvény alapú, valamint osztályozási elven működő identifikációs technikákra. A kutatások során vizsgáltjuk, hogyan lehet nemlineáris EIV modelleket becsülni mérési adatok alapján. Általánosítottuk a dinamikus lineáris modellekre kidolgozott EIV algoritmusokat polinomiális jellegű nemlineáris rendszerekre. Kutatást végeztünk robusztus predikciós irányítási algoritmusok lineáris és nemlineáris rendszerek irányítására területén. Lineáris rendszerekre vizsgáltuk a predikciós PID algoritmusok robusztusságát növelő módszereket. Lineáris rendszerek irányítására on-off predikciós szabályozások algoritmusainak kidolgozására került sor. Nemlineáris rendszerek irányítására – többek között - a parametrikus kvadratikus Volterra modellen alapuló szuboptimális algoritmust dolgoztunk ki. Továbbá a mérések feldolgozását segítő algoritmusokat kerestünk, amelyek segítésével hatékonyan megtalálhatók a statisztikai értelemben gyakori elemhalmazok, szekvenciák és részgráfok, valamint az adatok között fellelhető kapcsolatok, szabályok. | We have developed new algorithms for identifying errors-in-variables (EIV) models, which make it possible to estimate process and noise parameters simultaneously. By performing a principal component analysis or SVD separation on the signal space, the algorithms are able to compute process and noise parameters by comparing parameter estimates on the separated data. Furthermore, we have developed methods for objective function-based and classification-based identification techniques. We have investigated how we can identify nonlinear EIV models from measured data. We have generalized EIV identification methods for dynamic linear models to nonlinear systems with polynomial nonlinearities. We have conducted research concerning algorithms for robust predictive control in the field of linear and nonlinear systems control. For linear systems, we have investigated methods to increase the robustness of predictive PID algorithms. In the subfield of controlling linear systems, we have developed on/off predictive control algorithms, whereas in the subfield of nonlinear systems, we have devised – among others – a suboptimal algorithm based on a parametric quadratic Volterra model

    Kernel-based system identification from noisy and incomplete input-output data

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    In this contribution, we propose a kernel-based method for the identification of linear systems from noisy and incomplete input-output datasets. We model the impulse response of the system as a Gaussian process whose covariance matrix is given by the recently introduced stable spline kernel. We adopt an empirical Bayes approach to estimate the posterior distribution of the impulse response given the data. The noiseless and missing data samples, together with the kernel hyperparameters, are estimated maximizing the joint marginal likelihood of the input and output measurements. To compute the marginal-likelihood maximizer, we build a solution scheme based on the Expectation-Maximization method. Simulations on a benchmark dataset show the effectiveness of the method.Comment: 16 pages, submitted to IEEE Conference on Decision and Control 201

    Stochastic dynamic modeling of short gene expression time-series data

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    Copyright [2008] IEEE. This material is posted here with permission of the IEEE. Such permission of the IEEE does not in any way imply IEEE endorsement of any of Brunel University's products or services. Internal or personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution must be obtained from the IEEE by writing to [email protected]. By choosing to view this document, you agree to all provisions of the copyright laws protecting it.In this paper, the expectation maximization (EM) algorithm is applied for modeling the gene regulatory network from gene time-series data. The gene regulatory network is viewed as a stochastic dynamic model, which consists of the noisy gene measurement from microarray and the gene regulation first-order autoregressive (AR) stochastic dynamic process. By using the EM algorithm, both the model parameters and the actual values of the gene expression levels can be identified simultaneously. Moreover, the algorithm can deal with the sparse parameter identification and the noisy data in an efficient way. It is also shown that the EM algorithm can handle the microarray gene expression data with large number of variables but a small number of observations. The gene expression stochastic dynamic models for four real-world gene expression data sets are constructed to demonstrate the advantages of the introduced algorithm. Several indices are proposed to evaluate the models of inferred gene regulatory networks, and the relevant biological properties are discussed

    Model estimation of cerebral hemodynamics between blood flow and volume changes: a data-based modeling approach

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    It is well known that there is a dynamic relationship between cerebral blood flow (CBF) and cerebral blood volume (CBV). With increasing applications of functional MRI, where the blood oxygen-level-dependent signals are recorded, the understanding and accurate modeling of the hemodynamic relationship between CBF and CBV becomes increasingly important. This study presents an empirical and data-based modeling framework for model identification from CBF and CBV experimental data. It is shown that the relationship between the changes in CBF and CBV can be described using a parsimonious autoregressive with exogenous input model structure. It is observed that neither the ordinary least-squares (LS) method nor the classical total least-squares (TLS) method can produce accurate estimates from the original noisy CBF and CBV data. A regularized total least-squares (RTLS) method is thus introduced and extended to solve such an error-in-the-variables problem. Quantitative results show that the RTLS method works very well on the noisy CBF and CBV data. Finally, a combination of RTLS with a filtering method can lead to a parsimonious but very effective model that can characterize the relationship between the changes in CBF and CBV

    PNNARMA model: an alternative to phenomenological models in chemical reactors

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    This paper is focused on the development of non-linear neural models able to provide appropriate predictions when acting as process simulators. Parallel identification models can be used for this purpose. However, in this work it is shown that since the parameters of parallel identification models are estimated using multilayer feed-forward networks, the approximation of dynamic systems could be not suitable. The solution proposed in this work consists of building up parallel models using a particular recurrent neural network. This network allows to identify the parameter sets of the parallel model in order to generate process simulators. Hence, it is possible to guarantee better dynamic predictions. The dynamic behaviour of the heat transfer fluid temperature in a jacketed chemical reactor has been selected as a case study. The results suggest that parallel models based on the recurrent neural network proposed in this work can be seen as an alternative to phenomenological models for simulating the dynamic behaviour of the heating/cooling circuits.Publicad
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