Semi-Blind Robust Identification and Model Validation

We study a semi-blind robust identification
motivated from the fact that sometimes only partial
input data is exactly known. Derived from a
time-domain algorithm for robust identification, this
semi-blind robust identification
is stated as a non convex problem. We develop a
convex relaxation, by combining two variables into a
new variable, to reduce it to an LMI optimization
problem. Applying this convex relaxation, a
macro-economy modeling problem can be solved. The
problem of identification of Wiener Systems, a
special type of nonlinear systems, is analyzed from a
set-membership standpoint. We propose an algorithm
for time-domain based identification by pursuing a
risk-adjusted approach to reduce it to a convex
optimization problem. An arising non-trivial problem
in computer vision, tracking a human in a sequence of
frames, can be solved by modeling the plant as
Wiener system using the proposed identification
method. The book can serve as a reference for
financial engineers and finance-oriented
professionals in macro-economics and a textbook for
graduate courses on robust control theory and
macro-economics.

Autorentext
Ph.D.: Electrical Engineering at Pennsylvania State University (2007). B.Eng.: Electrical Engineering at Northeastern University (2002). Member of Sigma Xi and IEEE. Software Engineer at Yahoo! Inc., Sunnyvale, California.

Klappentext
We study a semi-blind robust identification motivated from the fact that sometimes only partial input data is exactly known. Derived from a time-domain algorithm for robust identification, this semi-blind robust identification is stated as a non convex problem. We develop a convex relaxation, by combining two variables into a new variable, to reduce it to an LMI optimization problem. Applying this convex relaxation, a macro-economy modeling problem can be solved. The problem of identification of Wiener Systems, a special type of nonlinear systems, is analyzed from a set-membership standpoint. We propose an algorithm for time-domain based identification by pursuing a risk-adjusted approach to reduce it to a convex optimization problem. An arising non-trivial problem in computer vision, tracking a human in a sequence of frames, can be solved by modeling the plant as Wiener system using the proposed identification method. The book can serve as a reference for financial engineers and finance-oriented professionals in macro-economics and a textbook for graduate courses on robust control theory and macro-economics.