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SUMMARY:A missing data approach to data-driven filtering and control - Iva
 n Markovsky\, Vrije Universiteit Brussel
DTSTART:20170227T120000Z
DTEND:20170227T130000Z
UID:TALK70922@talks.cam.ac.uk
CONTACT:Tim Hughes
DESCRIPTION:In filtering\, control\, and other mathematical engineering ar
 eas it is common to use a model-based approach\, which splits the problem 
 into two steps:\n 1) model identification and\n 2) model-based design.\nDe
 spite its success\, the model-based approach has the shortcoming that the 
 design objective is not taken into account at the identification step\, i.
 e.\, the model is not optimized for its intended use. In this talk\, I sho
 w a data-driven approach\, which combines the identification and the model
 -based design into one joint problem. The signal of interest is modeled as
  a missing part of a trajectory of the data generating system. Subsequentl
 y\, the missing data estimation problem is reformulated as a mosaic-Hankel
  structured matrix low-rank approximation/completion problem.\n\nThe missi
 ng data estimation approach for data-driven signal processing and a local 
 optimization method for its practical implementation are illustrated on ex
 amples of control\, state estimation\, filtering/smoothing\, and predictio
 n. Development of fast algorithms with provable properties in the presence
  of measurement noise and disturbances is a topic of current research.\n\n
 Reference: I. Markovsky. A missing data approach to data-driven filtering 
 and control. IEEE Trans. Automat. Contr.\, 2017. http://homepages.vub.ac.b
 e/~imarkovs/publications/ddsp.pdf
LOCATION:Cambridge University Engineering Department\, LR5
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