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Observation Noise (observation + noise)
Selected AbstractsData cloning: easy maximum likelihood estimation for complex ecological models using Bayesian Markov chain Monte Carlo methodsECOLOGY LETTERS, Issue 7 2007Subhash R. Lele Abstract We introduce a new statistical computing method, called data cloning, to calculate maximum likelihood estimates and their standard errors for complex ecological models. Although the method uses the Bayesian framework and exploits the computational simplicity of the Markov chain Monte Carlo (MCMC) algorithms, it provides valid frequentist inferences such as the maximum likelihood estimates and their standard errors. The inferences are completely invariant to the choice of the prior distributions and therefore avoid the inherent subjectivity of the Bayesian approach. The data cloning method is easily implemented using standard MCMC software. Data cloning is particularly useful for analysing ecological situations in which hierarchical statistical models, such as state-space models and mixed effects models, are appropriate. We illustrate the method by fitting two nonlinear population dynamics models to data in the presence of process and observation noise. [source] Identification of autoregressive models in the presence of additive noiseINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 5 2008Roberto Diversi Abstract A common approach in modeling signals in many engineering applications consists in adopting autoregressive (AR) models, consisting in filters with transfer functions having a unitary numerator, driven by white noise. Despite their wide application, these models do not take into account the possible presence of errors on the observations and cannot prove accurate when these errors are significant. AR plus noise models constitute an extension of AR models that consider also the presence of an observation noise. This paper describes a new algorithm for the identification of AR plus noise models that is characterized by a very good compromise between accuracy and efficiency. This algorithm, taking advantage of both low and high-order Yule,Walker equations, also guarantees the positive definiteness of the autocorrelation matrix of the estimated process and allows to estimate the equation error and observation noise variances. It is also shown how the proposed procedure can be used for estimating the order of the AR model. The new algorithm is compared with some traditional algorithms by means of Monte Carlo simulations. Copyright © 2007 John Wiley & Sons, Ltd. [source] Forecasting real-time data allowing for data revisionsJOURNAL OF FORECASTING, Issue 6 2007Kosei Fukuda Abstract A modeling approach to real-time forecasting that allows for data revisions is shown. In this approach, an observed time series is decomposed into stochastic trend, data revision, and observation noise in real time. It is assumed that the stochastic trend is defined such that its first difference is specified as an AR model, and that the data revision, obtained only for the latest part of the time series, is also specified as an AR model. The proposed method is applicable to the data set with one vintage. Empirical applications to real-time forecasting of quarterly time series of US real GDP and its eight components are shown to illustrate the usefulness of the proposed approach.,,Copyright © 2007 John Wiley & Sons, Ltd. [source] Robust face tracking control of a mobile robot using self-tuning Kalman filter and echo state network,ASIAN JOURNAL OF CONTROL, Issue 4 2010Chi-Yi Tsai Abstract This paper presents a novel design of face tracking algorithm and visual state estimation for a mobile robot face tracking interaction control system. The advantage of this design is that it can track a user's face under several external uncertainties and estimate the system state without the knowledge about target's 3D motion-model information. This feature is helpful for the development of a real-time visual tracking control system. In order to overcome the change in skin color due to light variation, a real-time face tracking algorithm is proposed based on an adaptive skin color search method. Moreover, in order to increase the robustness against colored observation noise, a new visual state estimator is designed by combining a Kalman filter with an echo state network-based self-tuning algorithm. The performance of this estimator design has been evaluated using computer simulation. Several experiments on a mobile robot validate the proposed control system. Copyright © 2010 John Wiley and Sons Asia Pte Ltd and Chinese Automatic Control Society [source] Variable structure robust state and parameter estimatorINTERNATIONAL JOURNAL OF ADAPTIVE CONTROL AND SIGNAL PROCESSING, Issue 2 2001Alex S. Poznyak Abstract The problem of simultaneous robust state and parameters estimation for a class of SISO non-linear systems under mixed uncertainties (unmodelled dynamics as well as observation noises) is addressed. A non-linear variable structure robust ,observer,identifier' is introduced to obtain the corresponding estimates. Lie derivative technique is used to obtain the observability conditions for the equivalent extended non-linear system. It is shown that, in general, the extended system can lose the global observability property and a special procedure is needed to work well in this situation. The suggested adaptive observer has the non-linear high-gain observer structure with adjusted parameters that provides ,a good' upper bound for the identification error performance index. The van der Monde transformation is used to derive this bound which turns out to be tight. Three examples dealing with a simple pendulum, the Duffing equation and the van del Pol oscillator are considered to illustrate the effectiveness of the suggested approach. Copyright © 2001 John Wiley & Sons, Ltd. [source] | ||||||||||