System identification from multiple short-time-duration signals.
System identification problems often arise where the only modeling records available consist of multiple short-time-duration signals. This motivates the development of a modeling approach that is tailored for this situation. An identification algorithm is presented here for parameter estimation based on minimizing the simulated prediction error, across multiple signals. The additional complexity of estimating the initial states corresponding to each signal is removed from the estimation algorithm. A numerical simulation demonstrates that the proposed algorithm performs well in comparison to the often-used least squares method (which leads to biased estimates when identifying systems from measurement noise corrupted signals). The approach is applied to the identification of the passive oculomotor plant; parameters are estimated that describe the dynamics of the plant, which represent the time constants of the visco-elastic elements that characterize the plant connective tissue.
Anderson
Sean R
SR
Neural Algorithms Research Group, Department of Psychology, University of Sheffield, Western Bank, Sheffield S10 2TP, U.K. s.anderson@sheffield.ac.uk
Dean
Paul
P
Kadirkamanathan
Visakan
V
Kaneko
Chris R S
CR
Porrill
John
J
D000465:Algorithms; D000818:Animals; D001185:Artificial Intelligence; D003198:Computer Simulation; D005133:Eye Movements / Q000502:physiology*; D008253:Macaca mulatta; D008954:Models, Biological; D010363:Pattern Recognition, Automated / Q000379:methods*; D012815:Signal Processing, Computer-Assisted; D013598:Systems Theory