Fast time-domain simulation for large-order linear time-invariant state space systems

2005 ◽  
Vol 63 (5) ◽  
pp. 681-708 ◽  
Author(s):  
Kin Cheong Sou ◽  
Olivier L. de Weck
Keyword(s):  
State Space ◽  
Time Domain ◽  
Linear Time ◽  
Fast Time ◽  
Space Systems ◽  
Time Domain Simulation ◽  
Invariant State ◽  
Linear Time Invariant ◽  
State Space Systems ◽  
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.

Time-domain noise analysis of linear time-Invariant and linear time-variant systems using MATLAB and HSPICE

2005 ◽  
Vol 52 (3) ◽  
pp. 805-812 ◽  
Author(s):  
S.C. Terry ◽  
B.J. Blalock ◽  
J.M. Rochelle ◽  
M.N. Ericson ◽  
S.D. Caylor
Keyword(s):  
Time Domain ◽  
Linear Time ◽  
Noise Analysis ◽  
Linear Time Invariant ◽  
Time Invariant

On krylov matrices and controllability of n-dimensional linear time-invariant state equations

1996 ◽  
Vol 3 (1-2) ◽  
pp. 99-109
Author(s):  
Andrzej Maćkiewicz ◽  
Francisco López Almansa ◽  
José A. Inaudi
Keyword(s):  
Linear Time ◽  
Invariant State ◽  
State Equations ◽  
Linear Time Invariant ◽  
Time Invariant
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