Model order reduction of fully parameterized systems by recursive least square optimization
Author(s)Zhang, Zheng; Elfadel, Ibrahim M.; Daniel, Luca
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This paper presents an approach for the model order reduction of fully parameterized linear dynamic systems. In a fully parameterized system, not only the state matrices, but also can the input/output matrices be parameterized. The algorithm presented in this paper is based on neither conventional moment-matching nor balanced-truncation ideas. Instead, it uses “optimal (block) vectors” to construct the projection matrix, such that the system errors in the whole parameter space are minimized. This minimization problem is formulated as a recursive least square (RLS) optimization and then solved at a low cost. Our algorithm is tested by a set of multi-port multi-parameter cases with both intermediate and large parameter variations. The numerical results show that high accuracy is guaranteed, and that very compact models can be obtained for multi-parameter models due to the fact that the ROM size is independent of the number of parameters in our approach.
DepartmentMassachusetts Institute of Technology. Department of Electrical Engineering and Computer Science; Massachusetts Institute of Technology. Research Laboratory of Electronics
2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD)
Zhang, Zheng, Ibrahim M. Elfadel, and Luca Daniel. “Model Order Reduction of Fully Parameterized Systems by Recursive Least Square Optimization.” 2011 IEEE/ACM International Conference on Computer-Aided Design (ICCAD) (November 2011). doi:10.1109/iccad.2011.6105380.
Author's final manuscript