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Real time input subset selection for linear time-variant MIMO systems. (English) Zbl 1120.92301

Summary: We propose an approach for multi-input multi-output (MIMO) system identification when the statistical relationship between input and output varies in input space as well as in time; i.e. nonstationary in space and time. An on-line variable selection algorithm, which has been recently developed for selecting a subset of input variables in real time by modifying least angle regression (LAR) with recursive estimators, is extensively applied to the linear time-variant MIMO systems. In our approach, a subset of input channels relevant with output is selected at every time instance based on the correlation between the filtering outcome of individual input channels and desired output. The on-line variable selection algorithm performs channel selection with weights using this real-time correlation. The proposed model is compared with a typical linear model in which only the least mean squares (LMS) is used to update system parameters. Tracking performances of these two models are demonstrated in a computer simulation and in a real-world application for tracking a linear relationship between neural firing rates of a primate and synchronously recorded hand kinematics. In both cases, our model demonstrates superior tracking performance.

MSC:

92B05 General biology and biomathematics
93B30 System identification
93E11 Filtering in stochastic control theory
93C05 Linear systems in control theory
93C35 Multivariable systems, multidimensional control systems
93E24 Least squares and related methods for stochastic control systems

Software:

alr3; ElemStatLearn
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References:

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