Exact calculations of discrete-time process noise statistics for hybrid continuous/discrete time applications

2002 ◽  
Author(s):  
J. Farrell ◽  
M. Livstone
Keyword(s):  
Discrete Time ◽  
Time Process ◽  
Process Noise ◽  
Exact Calculations ◽  
Noise Statistics

scholarly journals Stochastic stability of some state-dependent growth-collapse processes

2007 ◽  
Vol 39 (01) ◽  
pp. 189-220
Author(s):  
Christian Y. Robert
Keyword(s):  
Random Walk ◽  
Discrete Time ◽  
Power Law ◽  
Stochastic Stability ◽  
Long Range Dependence ◽  
Time Process ◽  
Power Law Distribution ◽  
State Dependent ◽  
Dependent Growth ◽  
Collapse Process

In this paper we consider a discrete-time process which grows according to a random walk with nonnegative increments between crash times at which it collapses to 0. We assume that the probability of crashing depends on the level of the process. We study the stochastic stability of this growth-collapse process. Special emphasis is given to the case in which the probability of crashing tends to 0 as the level of the process increases. In particular, we show that the process may exhibit long-range dependence and that the crash sizes may have a power law distribution.

scholarly journals The I2C-bus in discrete-time process algebra

1997 ◽  
Vol 29 (1-2) ◽  
pp. 235-258 ◽  
Author(s):  
S.H.J. Bos ◽  
M.A. Reniers
Keyword(s):  
Discrete Time ◽  
Process Algebra ◽  
Time Process ◽  
I2c Bus

scholarly journals Discrete time process algebra

1996 ◽  
Vol 8 (2) ◽  
pp. 188-208 ◽  
Author(s):  
J. C. M. Baeten ◽  
J. A. Bergstra
Keyword(s):  
Discrete Time ◽  
Process Algebra ◽  
Time Process

scholarly journals Stochastic stability of some state-dependent growth-collapse processes

2007 ◽  
Vol 39 (1) ◽  
pp. 189-220 ◽  
Author(s):  
Christian Y. Robert
Keyword(s):  
Random Walk ◽  
Discrete Time ◽  
Power Law ◽  
Stochastic Stability ◽  
Long Range Dependence ◽  
Time Process ◽  
Power Law Distribution ◽  
State Dependent ◽  
Dependent Growth ◽  
Collapse Process

In this paper we consider a discrete-time process which grows according to a random walk with nonnegative increments between crash times at which it collapses to 0. We assume that the probability of crashing depends on the level of the process. We study the stochastic stability of this growth-collapse process. Special emphasis is given to the case in which the probability of crashing tends to 0 as the level of the process increases. In particular, we show that the process may exhibit long-range dependence and that the crash sizes may have a power law distribution.

Simple random walk statistics. Part II: Continuous time results

1996 ◽  
Vol 33 (02) ◽  
pp. 331-339 ◽  
Author(s):  
W. Böhm ◽  
W. Panny
Keyword(s):  
Random Walk ◽  
Random Walks ◽  
Discrete Time ◽  
Continuous Time ◽  
Simple Random Walk ◽  
Laplace Transforms ◽  
Time Process ◽  
Basic Approach

In this paper various statistics for randomized random walks and their distributions are presented. The distributional results are derived by means of a limiting procedure applied to the pertaining discrete time process, which has been considered in part I of this work (Katzenbeisser and Panny 1996). This basic approach, originally due to Meisling (1958), seems to offer certain technical advantages, since it avoids the use of Laplace transforms and is even simpler than Feller's randomization technique.

Data Processing with an Innovation Self-Adaptive Denoising Amalgamation Algorithm

2014 ◽  
Vol 1003 ◽  
pp. 254-259
Author(s):  
Jian Yu Hu ◽  
Shu Ming Hou ◽  
Yan Fei Liu
Keyword(s):  
State Function ◽  
Observation Data ◽  
Optimal Solutions ◽  
Process Noise ◽  
Noise Statistic ◽  
Observation Noise ◽  
Non Linear System ◽  
Noise Statistics ◽  
Adaptive Denoising ◽  
Self Adaptive

The process noise and observation noise of a system are easily disturbed. It’s hard to know its statistic character. This paper proposes an innovation self-adaptive fading UPF algorithm to solve this problem. In the new algorithm, self-adaptive gradually vanishing UKF is used as weightiness density function of particle filter. New observation data is used to modify the error caused by state function of system and noise statistic parameter in time. What’s more, the new algorithm avoids traditional particle filter’s defect that it always gets part optimal solutions. Experiment results indicate that this new algorithm has a higher accuracy and robustness for the changeable noise statistics and non-linear system.

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