In this thesis, we study a so-called semi-blind robust identification motivated from the fact that sometimes for system Identification 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 modelling problem can be solved. For future work of application on Intrusion Detection, a sampling algorithm for blind identification is also briefly presented.

Accordingly, we consider the problem of semi-blind (in)validation which is shown to be non convex. Two different relaxations—a deterministic and a risk-adjusted convex relaxation—are explored to solve this non convex problem. We demonstrate an application of the semi-blind (in)validation on the problem of detecting and isolating faults from noisy input-output measurements. The results of this application using both two relaxations are presented through an experimental example.

Furthermore, 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 modelling the plant as Wiener system using the proposed identification method.