Results 211 to 220 of about 687,122 (255)
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On the stability of 2D state‐space models
Numerical Linear Algebra with Applications, 2011SUMMARYIn this paper, we consider the problem of stability of two‐dimensional linear systems. New sufficient conditions for the asymptotic stability are derived in terms of linear matrix inequalities. Copyright © 2011 John Wiley & Sons, Ltd.
Djilali Bouagada, Paul Van Dooren
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The likelihood for a state space model
Biometrika, 1988This paper derives an expression for the likelihood for a state space model. The expression can be evaluated with the Kalman filter initialized at a starting state estimate of zero and associated estimation error covariance matrix of zero. Adjustment for initial conditions can be made after filtering.
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STATE TRANSITION SPECIFICATION IN STATE‐SPACE MODELS
Journal of Time Series Analysis, 1986Abstract.This note investigates an approach to state transition specification in statespace models. The approach generalizes the procedure whereby first or higher order differences in the state are modelled as white noise.
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1998
In Chapter 1 we argued that mathematical models obtained from first principles usually contain latent variables. Up to now these latent variables did not enter the mathematical development. In Chapters 5 and 6 latent variable systems will be pursued in full generality. In the present chapter we discuss a special and important class of latent variables,
Jan Willem Polderman, Jan C. Willems
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In Chapter 1 we argued that mathematical models obtained from first principles usually contain latent variables. Up to now these latent variables did not enter the mathematical development. In Chapters 5 and 6 latent variable systems will be pursued in full generality. In the present chapter we discuss a special and important class of latent variables,
Jan Willem Polderman, Jan C. Willems
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2021
This chapter mentions the stochastic model and defines the state-space model as a stochastic model. We also explain the features and classification of the state-space model.
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This chapter mentions the stochastic model and defines the state-space model as a stochastic model. We also explain the features and classification of the state-space model.
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2017
The state space model is a very general model, mostly used to specify structural time-series models. Structural time-series models explicitly specify trends and seasonality along with other relevant influences. Under the classical Box-Jenkins time-series approach, in contrast, trends and seasonal influences are removed before estimating the core model.
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The state space model is a very general model, mostly used to specify structural time-series models. Structural time-series models explicitly specify trends and seasonality along with other relevant influences. Under the classical Box-Jenkins time-series approach, in contrast, trends and seasonal influences are removed before estimating the core model.
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A new online modelling method for aircraft engine state space model
Chinese Journal of Aeronautics, 2020Qiuhong Li, Shuwei Pang
exaly