Simulated Maximum Likelihood in Nonlinear Continuous-Discrete State Space Models: Importance Sampling by Approximate Smoothing

2003 ◽  
Vol 18 (1) ◽  
pp. 79-106 ◽  
Author(s):  
Hermann Singer
Keyword(s):  
Maximum Likelihood ◽  
State Space ◽  
Importance Sampling ◽  
State Space Models ◽  
Simulated Maximum Likelihood ◽  
Discrete State ◽  
Discrete State Space

scholarly journals Strict System Equivalence of 2D Linear Discrete State Space Models

2012 ◽  
Vol 2012 ◽  
pp. 1-6 ◽  
Author(s):  
Mohamed S. Boudellioua
Keyword(s):  
State Space ◽  
Polynomial Matrix ◽  
State Space Models ◽  
Exact Form ◽  
Discrete State ◽  
System Equivalence ◽  
Discrete State Space

The connection between the polynomial matrix descriptions (PMDs) of the well-known regular and singular 2D linear discrete state space models is considered. It is shown that the transformation of strict system equivalence in the sense of Fuhrmann provides the basis for this connection. The exact form of the transformation is established for both the regular and singular cases.

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.

Approximate linear minimum variance filters for continuous-discrete state space models: convergence and practical adaptive algorithms

2017 ◽  
Vol 36 (2) ◽  
pp. 341-378
Author(s):  
Juan Carlos Jimenez
Keyword(s):  
State Space ◽  
Adaptive Algorithms ◽  
Minimum Variance ◽  
State Equation ◽  
State Space Models ◽  
Discrete State ◽  
Stochastic Dynamical System ◽  
Conditional Moments ◽  
Time Distance ◽  
Discrete State Space

Abstract In this article, approximate linear minimum variance (LMV) filters for continuous-discrete state space models are introduced. The filters are derived from a wide class of recursive approximations to the predictions for the first two conditional moments of the state equation between each pair of consecutive observations. The convergence of the approximate filters to the exact LMV filter is proved when the error between the predictions and their approximations decreases no matter the time distance between observations. As particular instance, the order-$\beta$ local linearization filters are presented and expounded in detail. Practical adaptive algorithms are also provided and their performance in simulation is illustrated with various examples. The proposed filters are intended for the recurrent practical situation where a stochastic dynamical system should be identified from a reduced number of partial and noisy observations distant in time.

Sign in / Sign up

Export Citation Format

Share Document