A Nested Tensor Product Model Transformation

Yu, Yin; Li, Zhen; Liu, Xiangdong; Hirota, Kaoru; Chen, Xi; Fernando, Tyrone; Iu, Herbert Ho-Ching

doi:10.1109/tfuzz.2018.2851575

Cited by 46 publications

(20 citation statements)

References 42 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…and the Tensor Product Model Transformation (TPMT) [20], [21], [22], [23] can be employed to this end. Unlike [19] though, we explicitly consider the discretization error introduced by the sampling step of the TPMT, which are represented by Δ and Δ in (3).…”

Section: Discretization Strategymentioning

confidence: 99%

“…When such structure on the weighting functions is not needed, an approach similar to the 1-level Nested Tensor Product Model Transformation (NTPMT) [21] could be used instead, resulting in a tighter model with a smaller number of linear systems composing the desired model. We make use, instead, of the approach proposed in [19] since we have a particular structure on our domain.…”

Section: This Approach Is Usually Employed With Linear Parameter Varymentioning

confidence: 99%

“…The Tensor Product Model Transformation (TPMT) [20], [21], [22], [23], [24], [25] is a numerical technique that allows one to extract a convex representation, similar to a Takagi-Sugeno (TS) representation for a quasi-Linear Parameter Varying representation of a dynamical system. It makes use of the Higher-Order Singular Value Decomposition (HOSVD) to numerically extract a meaningful representation from a sampled representation of a function over a grid of possible values.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Analytical Upper Bound for the Error on the Discretization of Uncertain Linear Systems by using the Tensor Product Model Transformation

Campos¹,

Braga²,

Frezzatto³

2020

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

show abstract

Section: Discretization Strategymentioning

confidence: 99%

Section: This Approach Is Usually Employed With Linear Parameter Varymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Analytical Upper Bound for the Error on the Discretization of Uncertain Linear Systems by using the Tensor Product Model Transformation

Campos¹,

Braga²,

Frezzatto³

2020

ACTA POLYTECH HUNG

View full text Add to dashboard Cite

show abstract

“…FIOES uses a selective ensemble method that combines the ranking method [30], [31] with the iterative optimization method. The main steps are as follows:…”

Section: Filtering Iterative Optimization Ensemble Strategy 1) Selmentioning

confidence: 99%

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

2020

View full text Add to dashboard Cite

In the context power big data, the household power consumption data on the user side has the characteristics of large quantity, wide distribution and many types. Ensemble learning is very excellent in the analysis and mining of power data with large amount of data, strong timeliness and many influencing factors. Based on the data of household power consumption, this paper analyzes and predicts the power consumption of some users in a city by using selective ensemble learning technology and combining with meteorological factors. In this paper, K-means algorithm is used to cluster the household power consumption, and then the clustering results are combined with the meteorological information. In the stage of power consumption prediction, a Filter Iterative Optimization Ensemble Strategy (FIOES) is proposed to selectively ensemble the basic learners and get the final prediction model. Experimental results show that the FIOES algorithm has better performance in time cost and prediction accuracy than the traditional ensemble learning algorithm. INDEX TERMS Power big data, ensemble learning, power consumption, iterative optimization.

show abstract

“…On the other hand, the number of vertices and approximation error of TP model transformation can be effectively adjusted by varying the number of retained singular values. Therefore, it can achieve a trade-off between approximation accuracy and complexity [27], which has been already used in many nonlinear controls [28,26,29,30,31,32,33,34,35,36,37,38,39,40,41]. Hence, it is efficient and easy to be implemented.…”

Section: Introductionmentioning

confidence: 99%

Tensor Product Model-based Control for Spacecraft with Fuel Slosh Dynamics

2018

APH

View full text Add to dashboard Cite

Large spacecrafts undertaking long-life mission greatly suffer from the nonlinear fuel slosh when altering the orbit or maneuvering, which leads to the downgrade on the performance and even stability of the body attitude control by unintentionally generating the huge disturbance thrust. In order to specifically address this solid-liquid coupling problem for a kind of spacecrafts when altering orbit, a nonlinear control method based on Tensor Product (TP) Model transformation is proposed to make a quick response control of the translational velocity vector and the attitude of the spacecraft. Based on the derived polytopic system from TP model transformation, the controller design can guarantee the robust against the uncertainties and disturbances for all system sets within the bounds. The proposed solution also offers an approximation tradeoff so that both the complexity of the TP model and the controller design can be dramatically minimized. The simulation results for spacecrafts with practical specification verify that the design method can make the spacecraft asymptotically stable and demonstrate the effectiveness of the proposed controller.

show abstract

A Nested Tensor Product Model Transformation

Cited by 46 publications

References 42 publications

Analytical Upper Bound for the Error on the Discretization of Uncertain Linear Systems by using the Tensor Product Model Transformation

Analytical Upper Bound for the Error on the Discretization of Uncertain Linear Systems by using the Tensor Product Model Transformation

Household Power Consumption Prediction Method Based on Selective Ensemble Learning

Tensor Product Model-based Control for Spacecraft with Fuel Slosh Dynamics

Contact Info

Product

Resources

About