Nonlinear filtering for LIDAR signal processing.
Lainiotis, D.G., Papaparaskeva, Paraskevas, Plataniotis, Kostas (1996)
Mathematical Problems in Engineering
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Displaying similar documents to “Generalized kernel regression estimatefor the identification of Hammerstein systems”
Nonlinear filtering for LIDAR signal processing.
The strong pointwise convergence of nearest neighbor function fitting algorithm with applications to system identification
Alexander A. Georgiev (1985)
Kybernetika
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Recursive identification of Wiener systems
Włodzimierz Greblicki (2001)
International Journal of Applied Mathematics and Computer Science
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A Wiener system, i.e. a cascade system consisting of a linear dynamic subsystem and a nonlinear memoryless subsystem is identified. The a priori information is nonparametric, i.e. neither the functional form of the nonlinear characteristic nor the order of the dynamic part are known. Both the input signal and the disturbance are Gaussian white random processes. Recursive algorithms to estimate the nonlinear characteristic are proposed and their convergence is shown. Results of numerical...
State estimation of linear dynamic system with unknown input and uncertain observation using dynamic programming
Dariusz Janczak, Yuri Grishin (2006)
Control and Cybernetics
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Nonlinear state observers and extended Kalman filters for battery systems
Andreas Rauh, Saif S. Butt, Harald Aschemann (2013)
International Journal of Applied Mathematics and Computer Science
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The focus of this paper is to develop reliable observer and filtering techniques for finite-dimensional battery models that adequately describe the charging and discharging behaviors. For this purpose, an experimentally validated battery model taken from the literature is extended by a mathematical description that represents parameter variations caused by aging. The corresponding disturbance models account for the fact that neither the state of charge, nor the above-mentioned parameter...
Identifiability and estimation of pharmacokinetic parameters for the ligands of the macrophage mannose receptor
Nathalie Verdiere, Lilianne Denis-Vidal, Ghislaine Joly-Blanchard, Dominique Domurado (2005)
International Journal of Applied Mathematics and Computer Science
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The aim of this paper is numerical estimation of pharmacokinetic parameters of the ligands of the macrophage mannose receptor, without knowing it a priori the values of these parameters. However, it first requires a model identifiability analysis, which is done by applying an algorithm implemented in a symbolic computation language. It is shown that this step can lead to a direct numerical estimation algorithm. In this way, a first estimate is computed from noisy simulated observations...
Canonical input-output representation of linear multivariable stochastic systems and joint optimal parameter and state estimation.
G. Salut, J. Aguilar-Martín, S. Lefevre (1979)
Stochastica
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In this paper a complete presentation is given of a new canonical representation of multi-input, multi-output linear stochastic systems. Its equivalence with operator form directly linked with ARMA processes as well as with classical state space representation is given, and a transfer matrix interpretation is developed in an example. The importance of the new representation is mainly in the fact that in the joint state and parameters estimation problem, all unknown parameters appear...