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Semi-Blind Robust Identification and Model Validation Basics, Methods and Applications
Engleski
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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.
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