A Viterbi smoother for discrete state space model

2009 ◽  
Vol 58 (6) ◽  
pp. 400-405 ◽  
Author(s):  
Robert J. Elliott ◽  
Jia Deng
Keyword(s):  
State Space ◽  
State Space Model ◽  
Discrete State ◽  
Space Model ◽  
Discrete State Space

Responses comparison of the two discrete-time linear fractional state-space models

2016 ◽  
Vol 19 (4) ◽  
Author(s):  
Tadeusz Kaczorek ◽  
Piotr Ostalczyk
Keyword(s):  
State Space ◽  
State Space Model ◽  
State Space Models ◽  
Discrete State ◽  
Numerical Examples ◽  
Space Model ◽  
The Difference ◽  
Time Linear ◽  
Discrete State Space ◽  
Fundamental Matrices

AbstractIn this survey we consider two fractional-order discrete state-space models of linear systems. In both cases the crucial elements are the fundamental matrices. The difference between them is analyzed. A fundamental condition for the first state-space model is given. The investigations are illustrated by the numerical examples.

State Space Identification of Nonlinear Hydrodynamic Bearing by Eigensystem Realization Algorithm (ERA)

2003 ◽  
Author(s):  
T. N. Shiau ◽  
M. S. Tsai ◽  
C. H. Cheng
Keyword(s):  
State Space ◽  
State Space Model ◽  
Eigensystem Realization Algorithm ◽  
Discrete State ◽  
Hydrodynamic Bearing ◽  
Space Model ◽  
Space Technique ◽  
Rotor Bearing ◽  
Discrete State Space ◽  
Bearing System

Large vibration of a rotor-bearing system excited by unbalance of rotor shaft or external forces can deteriorate the performance and shorten the lifetime of the system. The hydrodynamic bearing can provide desirable damping for a rotor-bearing system. In order to fully utilize the function of the hydrodynamic bearing for vibration reduction, a state-space technique is developed to identify the parameters (stiffness and damping) of the linearized hydrodynamic bearing. The eigensystem realization algorithm (ERA) is adopted to find the discrete state space model of system. It is shown that the ERA approach can be a very effective way for identification of the rotor-bearing system. The discrete state space model is further transformed to the continuous model that can be utilized to obtain the coefficients for the hydrodynamic bearing system. By comparing the output signal of the identified system and the nonlinear rotor-bearing dynamic model, the identification accuracy is verified. More simulation results on different values of eccentricity are also plotted to show the characteristic of a hydrodynamic bearing.

A New Look at Urban Water Storage in a Series of Connected Dams

2014 ◽  
Vol 51 (01) ◽  
pp. 118-135
Author(s):  
Phil Howlett ◽  
Charles Pearce ◽  
Julia Piantadosi
Keyword(s):  
Steady State ◽  
State Space ◽  
State Space Model ◽  
Water Storage ◽  
Urban Water ◽  
Urban Stormwater ◽  
Discrete State ◽  
Space Model ◽  
Steady State Probabilities ◽  
Discrete State Space

We propose a discrete state-space model for storage of urban stormwater in two connected dams using an optimal pump-to-fill policy to transfer water from the capture dam to the holding dam. We assume stochastic supply to the capture dam and independent stochastic demand from the holding dam. We find new analytic formulae to calculate steady-state probabilities for the contents of each dam and thereby enable operators to better understand system behaviour. We illustrate our methods by considering some particular examples and discuss extension of our analysis to a series of three connected dams.

scholarly journals A New Look at Urban Water Storage in a Series of Connected Dams

2014 ◽  
Vol 51 (1) ◽  
pp. 118-135
Author(s):  
Phil Howlett ◽  
Charles Pearce ◽  
Julia Piantadosi
Keyword(s):  
Steady State ◽  
State Space ◽  
State Space Model ◽  
Water Storage ◽  
Urban Water ◽  
Urban Stormwater ◽  
Discrete State ◽  
Space Model ◽  
Steady State Probabilities ◽  
Discrete State Space

We propose a discrete state-space model for storage of urban stormwater in two connected dams using an optimal pump-to-fill policy to transfer water from the capture dam to the holding dam. We assume stochastic supply to the capture dam and independent stochastic demand from the holding dam. We find new analytic formulae to calculate steady-state probabilities for the contents of each dam and thereby enable operators to better understand system behaviour. We illustrate our methods by considering some particular examples and discuss extension of our analysis to a series of three connected dams.

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