Difference between revisions of "Sean G. Carver's Research Interests"
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== Pedagogy for Training Students on System Identification == | == Pedagogy for Training Students on System Identification == | ||
| − | I am interested in finding effective methods for training students in the art of system identification. SysID | + | I am interested in finding effective methods for training students in the art of system identification. Training students to use SysID is easier than might first be thought, because one can collect the experimental data needed from ''simulations''. For training and testing purposes, using a simulated system to generate the data is desired, because the experimenter then knows the correct model ahead of time. Knowing the correct model allows an assessment of the suitability and effectiveness of the SysID methods. Moreover, an instructor can create a dataset from simulation, but withhold from students the structure and parameterization of the generating model, giving students the more realistic experience of not already knowing the answer. One reason that this is such an advantage appears when you realize that an advanced laboratory technique can be trained without a large investment in a laboratory. |
Revision as of 19:52, 25 May 2013
Broadly, my interests involve system identification (SysID) applied to biological systems, especially:
- developing methods of SyID,
- applying methods of SysID to understand biological processes, and
- advancing methods for teaching SysID (pedagogy).
By system identification, I mean using statistics, derived from experimental data, to constrain models of the system.
System Identification of Cooperative Control
Control Theory Where Agent(s) Rely on Noisy Clocks
Pedagogy for Training Students on System Identification
I am interested in finding effective methods for training students in the art of system identification. Training students to use SysID is easier than might first be thought, because one can collect the experimental data needed from simulations. For training and testing purposes, using a simulated system to generate the data is desired, because the experimenter then knows the correct model ahead of time. Knowing the correct model allows an assessment of the suitability and effectiveness of the SysID methods. Moreover, an instructor can create a dataset from simulation, but withhold from students the structure and parameterization of the generating model, giving students the more realistic experience of not already knowing the answer. One reason that this is such an advantage appears when you realize that an advanced laboratory technique can be trained without a large investment in a laboratory.
