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 study a semi-blind robust identification motivated from the fact that sometimes only partial input data is exactly known. Derived from a time-domai...
