scholarly journals Parameter and state estimation in nonlinear stochastic continuous-time dynamic models with unknown disturbance intensity

2008 ◽  
Vol 86 (5) ◽  
pp. 828-837 ◽  
Author(s):  
M. S. Varziri ◽  
K. B. McAuley ◽  
P. J. McLellan
Keyword(s):  
State Estimation ◽  
Continuous Time ◽  
Dynamic Models ◽  
Time Dynamic ◽  
Disturbance Intensity ◽  
Unknown Disturbance

scholarly journals A Simple And Novel Algorithm For State Estimation Of Continuous Time Linear Stochastic Dynamic Systems Excited By Random Inputs

2019 ◽  
Vol 8 (10) ◽  
pp. 3559-3563
Keyword(s):  
Kalman Filter ◽  
State Estimation ◽  
Continuous Time ◽  
Stochastic Dynamic ◽  
Time Dynamic ◽  
Time Dynamics ◽  
Hardware Realization ◽  
Aerial Vehicle ◽  
Non Linear System ◽  
Stochastic Dynamic Systems

The major goal of this paper is to explore the effective state estimation algorithm for continuous time dynamic system under the lossy environment without increasing the complexity of hardware realization. Though the existing methods of state estimation of continuous time system provides effective estimation with data loss, the real time hardware realization is difficult due to the complexity and multiple processing. Kalman Filter and Particle Filer are fundamental algorithms for state estimation of any linear and non-linear system respectively, but both have its limitation. The approach adopted here, detect the expected state value and covariance, existed by random input at each stage and filtered the noisy measurement and replace it with predicted modified value for the effective state estimation. To demonstrate the performance of the results, the continuous time dynamics of position of the Aerial Vehicle is used with proposed algorithm under the lossy measurements scenario and compared with standard Kalman filter and smoothed filter. The results show that the proposed method can effectively estimate the position of Aerial Vehicle compared to standard Kalman and smoothed filter under the non-reliable sensor measurements with less hardware realization complexity.

Selecting optimal weighting factors in iPDA for parameter estimation in continuous-time dynamic models

2008 ◽  
Vol 32 (12) ◽  
pp. 3011-3022 ◽  
Author(s):  
M.S. Varziri ◽  
A.A. Poyton ◽  
K.B. McAuley ◽  
P.J. McLellan ◽  
J.O. Ramsay
Keyword(s):  
Parameter Estimation ◽  
Continuous Time ◽  
Dynamic Models ◽  
Time Dynamic ◽  
Weighting Factors ◽  
Optimal Weighting

scholarly journals Parameter estimation in continuous-time dynamic models using principal differential analysis

2006 ◽  
Vol 30 (4) ◽  
pp. 698-708 ◽  
Author(s):  
A.A. Poyton ◽  
M.S. Varziri ◽  
K.B. McAuley ◽  
P.J. McLellan ◽  
J.O. Ramsay
Keyword(s):  
Parameter Estimation ◽  
Continuous Time ◽  
Dynamic Models ◽  
Differential Analysis ◽  
Time Dynamic

The Estimation of Open Higher-Order Continuous Time Dynamic Models with Mixed Stock and Flow Data

1986 ◽  
Vol 2 (3) ◽  
pp. 350-373 ◽  
Author(s):  
A. R. Bergstrom
Keyword(s):  
Maximum Likelihood ◽  
Continuous Time ◽  
Dynamic Models ◽  
Higher Order ◽  
Maximum Likelihood Estimates ◽  
Order System ◽  
Exogenous Variables ◽  
Time Dynamic ◽  
Mixed Stock ◽  
Order Continuous

This article extends recent work on the Gaussian or quasi-maximum likelihood estimation of the parameters of a closed higher-order continuous time dynamic model by introducing exogenous variables into the model The method presented yields exact maximum likelihood estimates when the innovations are Gaussian and the exogenous variables are polynomials in time of degree not exceeding two, and it can be expected to yield very good estimates under more general conditions. It is applicable, in principle, to a system of any order with mixed stock and iow data. The precise formulas for its implementation are derived, in this article, for a second-order system in which both the endog-enous and exogenous variables are a mixture of stock and flow variables.

scholarly journals Controlling for cohort effects in accelerated longitudinal designs using continuous- and discrete-time dynamic models

2021 ◽  
Author(s):  
Eduardo Estrada ◽  
Silvia A. Bunge
Keyword(s):  
Continuous Time ◽  
Dynamic Models ◽  
Monte Carlo Study ◽  
Time Frame ◽  
Change Score ◽  
Cohort Effects ◽  
Time Dynamic ◽  
Cohort Differences ◽  
Study Designs ◽  
And Control

Accelerated longitudinal designs (ALDs) allow examining developmental changes over a period of time longer than the duration of the study. In ALDs, participants enter the study at different ages (i.e., different cohorts), and provide measures during a time frame shorter than the total study. They key assumption is that participants from the different cohorts come from the same population and, therefore, can be assumed to share the same general trajectory. The consequences of not meeting that assumption have not been examined systematically. In this paper, we propose an approach to detect and control for cohort differences in ALDs using Latent Change Score models in both discrete and continuous time. We evaluated the effectiveness of such a method through a Monte Carlo study. Our results indicate that, in a broad set of empirically relevant conditions, both LCS specifications can adequately estimate cohort effects ranging from very small to very large, with slightly better performance of the continuous-time version. Across all conditions, cohort effects on the asymptotic level (dAs) caused much larger bias than on the latent initial level (d0). When cohort differences were present, including them in the model led to unbiased estimates. In contrast, not including them led to tenable results only when such differences were not large (d0 ≤ 1 and dAs ≤ 0.2). Among the sampling schedules evaluated, those including at least three measurements per participant over 4 years or more led to the best performance. Based on our findings, we offer recommendations regarding study designs and data analysis.

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