Control Configuration Selection of Linear Multivariable Plants Based on the State-Space Models

2009 ◽  
pp. 115-138
Author(s):  
Ali Khaki-Sedigh ◽  
Bijan Moaveni
Keyword(s):  
State Space ◽  
The State ◽  
State Space Models ◽  
Configuration Selection ◽  
Control Configuration ◽  
Selection Of

scholarly journals A review of Bayesian state-space modelling of capture–recapture–recovery data

2012 ◽  
Vol 2 (2) ◽  
pp. 190-204 ◽  
Author(s):  
Ruth King
Keyword(s):  
State Space ◽  
State Space Model ◽  
Model Fitting ◽  
The State ◽  
State Space Models ◽  
Observation Error ◽  
Space Model ◽  
System Process ◽  
Observation Process ◽  
Capture Recapture

Traditionally, state-space models are fitted to data where there is uncertainty in the observation or measurement of the system. State-space models are partitioned into an underlying system process describing the transitions of the true states of the system over time and the observation process linking the observations of the system to the true states. Open population capture–recapture–recovery data can be modelled in this framework by regarding the system process as the state of each individual observed within the study in terms of being alive or dead, and the observation process the recapture and/or recovery process. The traditional observation error of a state-space model is incorporated via the recapture/recovery probabilities being less than unity. The models can be fitted using a Bayesian data augmentation approach and in standard BUGS packages. Applying this state-space framework to such data permits additional complexities including individual heterogeneity to be fitted to the data at very little additional programming effort. We consider the efficiency of the state-space model fitting approach by considering a random effects model for capture–recapture data relating to dippers and compare different Bayesian model-fitting algorithms within WinBUGS.

scholarly journals Unobserved Components Model for Forecasting Sugarcane Yield in Haryana

2019 ◽  
Vol 11 (3) ◽  
pp. 661-665 ◽  
Author(s):  
Ekta Hooda ◽  
Urmil Verma
Keyword(s):  
Time Series ◽  
State Space ◽  
The State ◽  
State Space Models ◽  
Stationary Time Series ◽  
Component Model ◽  
Series Data ◽  
Unobserved Component ◽  
Sugarcane Yield ◽  
Unobserved Component Model

Unlike classical regression analysis, the state space models have time-dependent parameters and provide a flexible class of dynamic and structural time series models. The unobserved component model (UCM) is a special type of state space models widely used to analyze and forecast time series. The present investigation has been carried out to study the trend of sugarcane(gur) yield in five districts (Ambala, Karnal, Panipat, Yamunanagar and Kurukshetra) of Haryana state using the unobserved component models with level, trend and irregular components. For this purpose, the time series data on sugarcane yield from 1966-67 to 2016-17 of Ambala and Karnal, 1971-72 to 2016-17 of Kurukshetra and 1980-81 to 2016-17 of Panipat and Yamunanagar districts have been used.   For all the districts, the irregular component was found to be highly significant (p=0.01) while both level and trend component variances were observed non-significant. Significance analysis of the individual component(s) has also been performed for possible dropping of the level and trend components by setting their variances equal to zero. The state space models may be effectively used pertaining to Indian agriculture data, as it takes into account the time dependency of the underlying parameters which may further enhance the predictive accuracy of the most popularly used ARIMA models with parameter constancy. Moreover, the unobserved component model is capable of handling both stationary as well as non-stationary time series and thus found more suitable for sugarcane yield modeling which is a trended yield (i.e. non-stationary in nature).

scholarly journals On the two-filter approximations of marginal smoothing distributions in general state-space models

2018 ◽  
Vol 50 (01) ◽  
pp. 154-177 ◽  
Author(s):  
Thi Ngoc Minh Nguyen ◽  
Sylvain Le Corff ◽  
Eric Moulines
Keyword(s):  
State Space ◽  
Posterior Distribution ◽  
The State ◽  
State Space Models ◽  
General State ◽  
Rigorous Analysis ◽  
The Past ◽  
Information Filter ◽  
General State Space ◽  
Filter Algorithms

AbstractA prevalent problem in general state-space models is the approximation of the smoothing distribution of a state conditional on the observations from the past, the present, and the future. The aim of this paper is to provide a rigorous analysis of such approximations of smoothed distributions provided by the two-filter algorithms. We extend the results available for the approximation of smoothing distributions to these two-filter approaches which combine a forward filter approximating the filtering distributions with a backward information filter approximating a quantity proportional to the posterior distribution of the state, given future observations.

Using an Extended Kalman Filter for Rigid Body Pose Estimation

2004 ◽  
Vol 127 (3) ◽  
pp. 475-483 ◽  
Author(s):  
Kjartan Halvorsen ◽  
Torsten Söderström ◽  
Virgil Stokes ◽  
Håkan Lanshammar
Keyword(s):  
Kalman Filter ◽  
Rigid Body ◽  
State Space ◽  
Pose Estimation ◽  
Measurement Errors ◽  
The State ◽  
State Space Models ◽  
Marker Data ◽  
Measurement Function ◽  
Body Pose Estimation

Rigid body pose is commonly represented as the rigid body transformation from one (often reference) pose to another. This is usually computed for each frame of data without any assumptions or restrictions on the temporal change of the pose. The most common algorithm was proposed by Söderkvist and Wedin (1993, “Determining the Movements of the Skeleton Using Well-configured Markers,” J. Biomech., 26, pp. 1473–1477), and implies the assumption that measurement errors are isotropic and homogenous. This paper describes an alternative method based on a state space formulation and the application of an extended Kalman filter (EKF). State space models are formulated, which describe the kinematics of the rigid body. The state vector consists of six generalized coordinates (corresponding to the 6 degrees of freedom), and their first time derivatives. The state space models have linear dynamics, while the measurement function is a nonlinear relation between the state vector and the observations (marker positions). An analytical expression for the linearized measurement function is derived. Tracking the rigid body motion using an EKF enables the use of a priori information on the measurement noise and type of motion to tune the filter. The EKF is time variant, which allows for a natural way of handling temporarily missing marker data. State updates are based on all the information available at each time step, even when data from fewer than three markers are available. Comparison with the method of Söderkvist and Wedin on simulated data showed a considerable improvement in accuracy with the proposed EKF method when marker data was temporarily missing. The proposed method offers an improvement in accuracy of rigid body pose estimation by incorporating knowledge of the characteristics of the movement and the measurement errors. Analytical expressions for the linearized system equations are provided, which eliminate the need for approximate discrete differentiation and which facilitate a fast implementation.

On the selection of the state space in nonequilibrium thermodynamics

1998 ◽  
Vol 248 (1-2) ◽  
pp. 111-137 ◽  
Author(s):  
Roberto Luzzi ◽  
Aurea R. Vasconcellos ◽  
José Casas-Vázquez ◽  
David Jou
Keyword(s):  
State Space ◽  
Nonequilibrium Thermodynamics ◽  
The State ◽  
Selection Of

Evaluating abundance and trends in a Hawaiian avian community using state-space analysis

2015 ◽  
Vol 26 (2) ◽  
pp. 225-242 ◽  
Author(s):  
RICHARD J. CAMP ◽  
KEVIN W. BRINCK ◽  
P. MARCOS GORRESEN ◽  
EBEN H. PAXTON
Keyword(s):  
Linear Regression ◽  
State Space ◽  
Process Variation ◽  
The State ◽  
State Space Models ◽  
Conservation Strategies ◽  
Avian Community ◽  
Abundance Estimates ◽  
Trend Assessment ◽  
Population Trajectory

SummaryEstimating population abundances and patterns of change over time are important in both ecology and conservation. Trend assessment typically entails fitting a regression to a time series of abundances to estimate population trajectory. However, changes in abundance estimates from year-to-year across time are due to both true variation in population size (process variation) and variation due to imperfect sampling and model fit. State-space models are a relatively new method that can be used to partition the error components and quantify trends based only on process variation. We compare a state-space modelling approach with a more traditional linear regression approach to assess trends in uncorrected raw counts and detection-corrected abundance estimates of forest birds at Hakalau Forest National Wildlife Refuge, Hawai‘i. Most species demonstrated similar trends using either method. In general, evidence for trends using state-space models was less strong than for linear regression, as measured by estimates of precision. However, while the state-space models may sacrifice precision, the expectation is that these estimates provide a better representation of the real world biological processes of interest because they are partitioning process variation (environmental and demographic variation) and observation variation (sampling and model variation). The state-space approach also provides annual estimates of abundance which can be used by managers to set conservation strategies, and can be linked to factors that vary by year, such as climate, to better understand processes that drive population trends.

scholarly journals Development of State Space Models with Weather as Exogenous Input for Sugarcane Yield Prediction in Haryana

2020 ◽  
pp. 6-13
Author(s):  
Ekta Hooda ◽  
Urmil Verma
Keyword(s):  
State Space ◽  
The State ◽  
State Space Models ◽  
Yield Prediction ◽  
Structural Time Series ◽  
Export Decisions ◽  
Course Of Action ◽  
Sugarcane Yield ◽  
Procurement Price ◽  
Structural Time Series Models

Parameter constancy is a fundamental issue for empirical models to be useful for forecasting, analyzing or testing any theory. Unlike classical regression analysis, the state space models (SSM) are time varying parameters models as they allow for known changes in the structure of the system over time and provide a flexible class of dynamic and structural time series models. The work deals with the development of state space models with weather as exogenous input for sugarcane yield prediction in Ambala and Karnal districts of Haryana. The state space models with weather as exogenous input using different types of growth trends viz., polynomial splines; PS(1), PS(2) and PS(3) have been developed however PS(2) with weather input was selected as the best suited model for this empirical study. Timely and effective pre-harvest forecast of crop yield helps in advance planning, formulation and implementation of policies related to the crop procurement, price structure, distribution and import-export decisions etc. These forecasts are also useful to farmers to decide in advance their future prospects and course of action. The sugarcane yield forecasts based on state space models with weather input showed good agreement with state Department of Agriculture and Farmers’ Welfare yield(s) by showing nearly 4 percent average absolute relative deviations in the two districts.

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