首页|Identification of piecewise affine model for batch processes based on constrained clustering technique
Identification of piecewise affine model for batch processes based on constrained clustering technique
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NSTL
In this paper, a novel identification method of piecewise affine (PWA) model for batch processes based on constrained clustering technique is proposed. In traditional clustering-based identification approaches, data classification and region partition are performed individually so that inseparable problem usually occurs in the partition phase. The proposed method uses a constrained K-means clustering algorithm to simultaneously perform both data classification and region partition, which is accomplished by imposing the complete and non-overlapping partition constraints into the clustering optimization problem. We employ a greedy iterative approach combined with the golden section search to efficiently solve the constrained clustering problem. This method can greatly improve the accuracy of the identified PWA model. Finally, we demonstrate the effectiveness of the proposed identification method.
State Key Laboratory of Industrial Control Technology, College of Control Science and Engineering, Zhejiang University, Hangzhou, Peoples' Republic of China
NSTL
