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TP Model Transformation Via Sequentially Truncated Higher‐Order Singular Value Decomposition
Author(s) -
Pan Junjun,
Lu Linzhang
Publication year - 2015
Publication title -
asian journal of control
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.769
H-Index - 53
eISSN - 1934-6093
pISSN - 1561-8625
DOI - 10.1002/asjc.1043
Subject(s) - weighting , transformation (genetics) , mathematics , singular value decomposition , decomposition , tensor product , mathematical optimization , reduction (mathematics) , model transformation , algorithm , tensor (intrinsic definition) , pure mathematics , discrete mathematics , medicine , ecology , biochemistry , chemistry , geometry , consistency (knowledge bases) , radiology , biology , gene
The sequentially truncated higher‐order singular value decomposition (ST‐HOSVD) is applied to a tensor product (TP) model transformation instead of the compact form of HOSVD (CHOSVD). The goal is to reduce computational cost in the transformation. By using the ST‐HOSVD, the TP model transformations of systems and the related algorithms are executed and the ST‐HOSVD based canonical form and the weighting functions are given. To see the effectiveness, we take a dynamic system and TORA system as numerical examples. A great reduction of complexity is seen in use of the ST‐HOSVD compared with use of the CHOSVD in TP model transformation. The approximation of the new method seems as good as the original one.