Online Spatiotemporal Least-Squares Support Vector Machine Modeling Approach for Time-Varying Distributed Parameter Processes

2017 ◽  
Vol 56 (25) ◽  
pp. 7314-7321 ◽  
Author(s):  
XinJiang Lu ◽  
Feng Yin ◽  
MingHui Huang
Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 453
Author(s):  
Ling Ai ◽  
Yang Xu ◽  
Liwei Deng ◽  
Kok Lay Teo

This manuscript addresses a new multivariate generalized predictive control strategy using the least squares support vector machine for parabolic distributed parameter systems. First, a set of proper orthogonal decomposition-based spatial basis functions constructed from a carefully selected set of data is used in a Galerkin projection for the building of an approximate low-dimensional lumped parameter systems. Then, the temporal autoregressive exogenous model obtained by the least squares support vector machine is applied in the design of a multivariate generalized predictive control strategy. Finally, the effectiveness of the proposed multivariate generalized predictive control strategy is verified through a numerical simulation study on a typical diffusion-reaction process in radical symmetry.


2009 ◽  
Vol 35 (2) ◽  
pp. 214-219 ◽  
Author(s):  
Xue-Song WANG ◽  
Xi-Lan TIAN ◽  
Yu-Hu CHENG ◽  
Jian-Qiang YI

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