Risk Adjusted Identification of Wiener Systems

This paper addresses the problem of identification of Wiener systems from a set-membership standpoint. Its main result shows that, by pursuing a risk-adjusted approach, the problem can be reduced to a convex LMI optimization form that can be efficiently solved. These results are illustrated with a non-trivial problem arising in computer vision: tracking a human in a sequence of frames, where the challenge here arises from the changes in appearance undergone by the target and the large number of pixels to be tracked