A Nested Tensor Product Model Transformation

Yin Yu, Zhen Li, Xiangdong Liu, Kaoru Hirota, Xi Chen, Tyrone Fernando, Herbert Iu

Research output: Contribution to journalArticlepeer-review

39 Citations (Web of Science)


The tensor product model transformation (TPMT) is an emerging numerical framework of Takagi-Sugeno (T-S) fuzzy (or polytopic) system modeling for LMI-based system control design. A nested TPMT (NTPMT) is proposed in this paper, which merges the dimensions of the tensors and performs the TPMT iteratively. The resultant fuzzy model is in a multi-level nested tensor product (TP) structure. The vertex tensor obtained by NTPMT has less dimensions than the original TPMT results so that the number of vertices or fuzzy rules, which has been the main bottleneck for further application of the TPMT in higher dimensional systems, is expected to decrease manyfold. It is also proved that the NTPMT contains the hierarchical fuzzy logic, which means that the NTPMT is capable of conducting hierarchical fuzzy modeling and reduction. Furthermore, because the inclusion of multiple TPMTs is prone to augment the conservativeness of the resultant fuzzy model, a suboptimal convex hull rectification algorithm for the TPMT is developed based on a newly defined tightness measure and then extended to render the NTPMT as less conservative as possible. Finally, numerical simulations on two real physical systems (2- and 4-parameter-dimension) are verified to demonstrate the performance of the methods.

Original languageEnglish
Article number8400489
Pages (from-to)1-15
Number of pages15
JournalIEEE Transactions on Fuzzy Systems
Issue number1
Early online date29 Jun 2018
Publication statusPublished - Jan 2019


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