Time Invariant State Space Models

2016 ◽  
pp. 323-403
Author(s):  
Víctor Gómez
Keyword(s):  
State Space ◽  
State Space Models ◽  
Invariant State ◽  
Time Invariant

Optimal experimental design for linear time invariant state–space models

2021 ◽  
Vol 31 (4) ◽  
Author(s):  
Belmiro P. M. Duarte ◽  
Anthony C. Atkinson ◽  
Nuno M. C. Oliveira
Keyword(s):  
Experimental Design ◽  
State Space ◽  
Linear Time ◽  
State Space Models ◽  
Optimal Experimental Design ◽  
Invariant State ◽  
Linear Time Invariant ◽  
Time Invariant

A fast and stable method to compute the likelihood of time invariant state-space models

1999 ◽  
Vol 65 (3) ◽  
pp. 329-337 ◽  
Author(s):  
José Casals ◽  
Sonia Sotoca ◽  
Miguel Jerez
Keyword(s):  
State Space ◽  
State Space Models ◽  
Invariant State ◽  
Stable Method ◽  
Time Invariant

scholarly journals Fast bias-constrained optimal FIR filtering for time-invariant state space models

2016 ◽  
Vol 31 (7) ◽  
pp. 1061-1076 ◽  
Author(s):  
Shunyi Zhao ◽  
Yuriy S. Shmaliy ◽  
Guoli Ji ◽  
Sanowar H. Khan
Keyword(s):  
State Space ◽  
State Space Models ◽  
Invariant State ◽  
Fir Filtering ◽  
Time Invariant

scholarly journals Explaining Savings for Visuomotor Adaptation: Linear Time-Invariant State-Space Models Are Not Sufficient

2008 ◽  
Vol 100 (5) ◽  
pp. 2537-2548 ◽  
Author(s):  
Eric Zarahn ◽  
Gregory D. Weston ◽  
Johnny Liang ◽  
Pietro Mazzoni ◽  
John W. Krakauer
Keyword(s):  
State Space ◽  
Linear Time ◽  
State Space Model ◽  
Visuomotor Adaptation ◽  
Invariant State ◽  
Visuomotor Rotation ◽  
Body Memory ◽  
Linear Time Invariant ◽  
And Coping ◽  
Time Invariant

Adaptation of the motor system to sensorimotor perturbations is a type of learning relevant for tool use and coping with an ever-changing body. Memory for motor adaptation can take the form of savings: an increase in the apparent rate constant of readaptation compared with that of initial adaptation. The assessment of savings is simplified if the sensory errors a subject experiences at the beginning of initial adaptation and the beginning of readaptation are the same. This can be accomplished by introducing either 1) a sufficiently small number of counterperturbation trials (counterperturbation paradigm [ CP]) or 2) a sufficiently large number of zero-perturbation trials (washout paradigm [ WO]) between initial adaptation and readaptation. A two-rate, linear time-invariant state-space model (SSMLTI,2) was recently shown to theoretically produce savings for CP. However, we reasoned from superposition that this model would be unable to explain savings for WO. Using the same task (planar reaching) and type of perturbation (visuomotor rotation), we found comparable savings for both CP and WO paradigms. Although SSMLTI,2 explained some degree of savings for CP it failed completely for WO. We conclude that for visuomotor rotation, savings in general is not simply a consequence of LTI dynamics. Instead savings for visuomotor rotation involves metalearning, which we show can be modeled as changes in system parameters across the phases of an adaptation experiment.

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