Discrete-Time Estimation of Nonlinear Continuous-Time Stochastic Systems

2015 ◽  
pp. 91-104
Author(s):  
Mariusz Domżalski ◽  
Zdzisław Kowalczuk
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Stochastic Systems ◽  
Time Estimation

Non-parametric covariance estimation from irregularly-spaced data

1983 ◽  
Vol 15 (01) ◽  
pp. 113-132 ◽  
Author(s):  
Elias Masry
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Covariance Function ◽  
Time Estimation ◽  
Covariance Estimation ◽  
Consistent Estimation ◽  
Asymptotic Statistics ◽  
Continuous Time Processes ◽  
Non Parametric

The non-parametric discrete-time estimation of the covariance function R(t) of stationary continuous-time processes is considered. The characteristics of the sampling instants necessary for the consistent estimation of R(t) are explored. A class of covariance estimates is introduced and its asymptotic statistics are derived.

scholarly journals Algorithms to Solve StochasticH2/H∞Control with State-Dependent Noise

2014 ◽  
Vol 2014 ◽  
pp. 1-9
Author(s):  
Ming Gao ◽  
Changdi Fu ◽  
Weihai Zhang
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Stochastic Systems ◽  
Infinite Horizon ◽  
Control Problems ◽  
Numerical Examples ◽  
State Dependent

This paper is concerned with the algorithms which solveH2/H∞control problems of stochastic systems with state-dependent noise. Firstly, the algorithms for the finite and infinite horizonH2/H∞control of discrete-time stochastic systems are reviewed and studied. Secondly, two algorithms are proposed for the finite and infinite horizonH2/H∞control of continuous-time stochastic systems, respectively. Finally, several numerical examples are presented to show the effectiveness of the algorithms.

Non-parametric covariance estimation from irregularly-spaced data

1983 ◽  
Vol 15 (1) ◽  
pp. 113-132 ◽  
Author(s):  
Elias Masry
Keyword(s):  
Discrete Time ◽  
Continuous Time ◽  
Covariance Function ◽  
Time Estimation ◽  
Covariance Estimation ◽  
Consistent Estimation ◽  
Asymptotic Statistics ◽  
Continuous Time Processes ◽  
Non Parametric

The non-parametric discrete-time estimation of the covariance function R(t) of stationary continuous-time processes is considered. The characteristics of the sampling instants necessary for the consistent estimation of R(t) are explored. A class of covariance estimates is introduced and its asymptotic statistics are derived.

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