Journal ArticleDOI
Special section system identification tutorial: Maximum likelihood and prediction error methods
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TLDR
The basic ideas behind the parameter estimation methods are discussed in a general setting and an example is given which illustrates some properties of the methods and shows the usefulness of interactive computing.About:
This article is published in Automatica.The article was published on 1980-09-01. It has received 372 citations till now. The article focuses on the topics: Estimation theory & System identification.read more
Citations
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Water quality modeling: A review of the analysis of uncertainty
TL;DR: A review of the role of uncertainty in the identification of mathematical models of water quality and in the application of these models to problems of prediction can be found in this paper, where four problem areas are examined in detail: uncertainty about model structure, uncertainty in estimated model parameter values, the propagation of prediction errors, and the design of experiments in order to reduce the critical uncertainties associated with a model.
Journal ArticleDOI
Using Inertial Sensors for Position and Orientation Estimation
TL;DR: In recent years, micro-machined electromechanical system inertial sensors (3D accelerometers and 3D gyroscopes) have become widely available due to their small size and low cost.
Journal ArticleDOI
Blur identification by the method of generalized cross-validation
TL;DR: Experiments are presented which show that GVC is capable of yielding good identification results and a comparison of the GCV criterion with maximum-likelihood (ML) estimation shows theGCV often outperforms ML in identifying the blur and image model parameters.
Journal ArticleDOI
Asymptotic variance expressions for identified black-box transfer function models
TL;DR: The result is that the joint covariance matrix of the transfer functions from input to output and from driving white noise source to the additive output disturbance, respectively, is proportional to the inverse of the joint spectrum matrix for the input and driving noise multiplied by the spectrum of the additiveoutput noise.
Journal ArticleDOI
Identification of linear stochastic systems via second- and fourth-order cumulant matching
TL;DR: The identification problem for time-invariant single-input single-output linear stochastic systems driven by non-Gaussian white noise is considered and a least-squares criterion that involves matching the second- and the fourth-order cumulant functions of the noisy observations is proposed.
References
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Journal ArticleDOI
A new look at the statistical model identification
TL;DR: In this article, a new estimate minimum information theoretical criterion estimate (MAICE) is introduced for the purpose of statistical identification, which is free from the ambiguities inherent in the application of conventional hypothesis testing procedure.
Book
Time series analysis, forecasting and control
TL;DR: In this article, a complete revision of a classic, seminal, and authoritative book that has been the model for most books on the topic written since 1970 is presented, focusing on practical techniques throughout, rather than a rigorous mathematical treatment of the subject.
Proceedings Article
Information Theory and an Extention of the Maximum Likelihood Principle
TL;DR: The classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion to provide answers to many practical problems of statistical model fitting.
Book ChapterDOI
Information Theory and an Extension of the Maximum Likelihood Principle
TL;DR: In this paper, it is shown that the classical maximum likelihood principle can be considered to be a method of asymptotic realization of an optimum estimate with respect to a very general information theoretic criterion.