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Convex Optimization Methods for System Identification
Linnaeus University, Faculty of Technology, Department of Physics and Electrical Engineering.
2014 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

The extensive use of a least-squares problem formulation in many fields is partly motivated by the existence of an analytic solution formula which makes the theory comprehensible and readily applicable, but also easily embedded in computer-aided design or analysis tools. While the mathematics behind convex optimization has been studied for about a century, several recent researches have stimulated a new interest in the topic. Convex optimization, being a special class of mathematical optimization problems, can be considered as generalization of both least-squares and linear programming. As in the case of a linear programming problem there is in general no simple analytical formula that can be used to find the solution of a convex optimization problem. There exists however efficient methods or software implementations for solving a large class of convex problems. The challenge and the state of the art in using convex optimization comes from the difficulty in recognizing and formulating the problem. The main goal of this thesis is to investigate the potential advantages and benefits of convex optimization techniques in the field of system identification. The primary work focuses on parametric discrete-time system identification models in which we assume or choose a specific model structure and try to estimate the model parameters for best fit using experimental input-output (IO) data. By developing a working knowledge of convex optimization and treating the system identification problem as a convex optimization problem will allow us to reduce the uncertainties in the parameter estimation. This is achieved by reecting prior knowledge about the system in terms of constraint functions in the least-squares formulation.

Place, publisher, year, edition, pages
2014. , 50 p.
Keyword [en]
Mathematical Optimization, Convex Optimization, Least-Squares, LTI-System, System Identification, Inverse System
National Category
Signal Processing
Identifiers
URN: urn:nbn:se:lnu:diva-34808OAI: oai:DiVA.org:lnu-34808DiVA: diva2:722574
External cooperation
Combitech (IS)
Subject / course
Electrical Engineering
Educational program
Electrical Engineering with specialisation in Signal Processing & Wave Propagation, Master Programme, 120 credits
Presentation
2014-06-03, D0043, Växjö, 10:59 (English)
Supervisors
Examiners
Available from: 2014-06-09 Created: 2014-06-09 Last updated: 2014-06-09Bibliographically approved

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