The minimization of measurement error in a general perturbation-correlation process identification system

Perlis, H. J.

doi:10.1109/tac.1964.1105750

IEEE Trans. Automat. Contr.

1964

DOI: 10.1109/tac.1964.1105750

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The minimization of measurement error in a general perturbation-correlation process identification system

H. J. Perlis

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Cited by 2 publications

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Bayesian State Estimation of Nonlinear Systems Using Approximate Aggregate Markov Chains

Ungarala

Chen

2006

Ind. Eng. Chem. Res.

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The conditional probability density function (pdf) is the most complete statistical representation of the state from which optimal inferences may be drawn. The transient pdf is usually infinite-dimensional and impossible to obtain except for linear Gaussian systems. In this paper, a novel density-based filter is proposed for nonlinear Bayesian estimation. The approach is fundamentally different from optimization- and linearization-based methods. Unlike typical density-based methods such as probability grid filters (PGF) and sequential Monte Carlo (SMC), the cell filter poses an off-line probabilistic modeling task and an on-line estimation task. The probabilistic behavior is described by the Foias or Frobenius−Perron operators. Monte Carlo simulations are used for computing these transition operators, which represent approximate aggregate Markov chains. The approach places no restrictions on system model or noise processes. The cell filter is shown to achieve the performance of PGF and SMC filters at a fraction of the computational cost for recursive state estimation.

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Bayesian State Estimation of Nonlinear Systems Using Approximate Aggregate Markov Chains

Ungarala

Chen

2006

Ind. Eng. Chem. Res.

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On the relative time of adaptive processes

Chang

1965

IEEE Trans. Automat. Contr.

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Absiraci-Adaptive control systems can be classified according to the response time of the adaptive loop T, relative to that of the main servo loop T,. The response time T, cannot be smaller than the time needed for making the required measurement on which the adjustments are based.In a slow adaptive system, T,>>T,, and the adjustments are made according to the estimated situation (statistical parameters of the inputs changing plant parameters, etc.) or the estimated performance (error, cost, intermediate parameters etc.). If T,<>T, and T,<

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The minimization of measurement error in a general perturbation-correlation process identification system

Cited by 2 publications

References 11 publications

Bayesian State Estimation of Nonlinear Systems Using Approximate Aggregate Markov Chains

Bayesian State Estimation of Nonlinear Systems Using Approximate Aggregate Markov Chains

On the relative time of adaptive processes

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