Tail Deviation’s Predictive Control of the Tandem Rolling Strip Based on Manifold Learning

Guang Bin Wang; Y.Q. Kong; Ke Wang

doi:10.4028/www.scientific.net/AEF.2-3.63

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HomeAdvanced Engineering ForumAdvanced Engineering Forum Vols. 2-3Tail Deviation’s Predictive Control of the Tandem...

Tail Deviation’s Predictive Control of the Tandem Rolling Strip Based on Manifold Learning

Abstract:

In the rolling process, serious deviation will cause product quality drop and rolling equipment fault. This reserch propose tail deviation’s predictive control method of the tandem rolling strip based on manifold learning. Based on real deviation data in the rolling production site,tail deviation patterns are divided according to deviation’s value. Using manifold learning method to deviation data in middle rolling stage , tail deviation pattern and scope are obtained. According to regression model between the control variable and deviation, predictive control strategy of the tandem rolling strip may be implemented. Experiment shows this method may control tail deviation in preconcerted permission range.

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Periodical:

Advanced Engineering Forum (Volumes 2-3)

Pages:

63-68

DOI:

https://doi.org/10.4028/www.scientific.net/AEF.2-3.63

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Online since:

December 2011

Authors:

Guang Bin Wang, Y.Q. Kong, Ke Wang

Keywords:

Manifold Learning, Predictive Control, Tail Deviation, Tandem Rolling Strip

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Creative Commons CC BY 4.0

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References

[1] Seung H.S., Daniel D.L., The manifold ways of perception, Science, 290(5500), 2268 -2269(2000).

DOI: 10.1126/science.290.5500.2268

Google Scholar

[2] Roweis,S., Saul,L., Nonlinear dimensionality reduction by locally linear embedding, Science, 290(5500), 2323-2326(2000).

DOI: 10.1126/science.290.5500.2323

Google Scholar

[3] Belkin,M., Niyogi,P., Laplacian eigenmaps for dimensionality reduction and data representation, ]. Neural Computation, 15(6), 1373-1396(2003).

DOI: 10.1162/089976603321780317

Google Scholar

[4] Zhang Zhenyue, Zha Hongyuan, Principal manioflds and nonlinear dimension reduction via Tangent Space Alignmnet, ，SIAM Journal on Scientific Computing, 26(1), 313-338(2005).

DOI: 10.1137/s1064827502419154

Google Scholar

[5] He Xiaofei, Niyogi,P., Locality preserving projections, Advances in Neural Information Processing Systems, MIT Press, cambrifge, MA, (2004).

Google Scholar

[6] Olga,K., Oleg,O., Matti Pietikäinen. Supervised locally linear embedding algorithm for pattern recognition, Pattern Recognition and Image Analysis, 2652(9), 386-394(2003).

DOI: 10.1007/978-3-540-44871-6_45

Google Scholar

[7] Belkin,M., Niyogi,P., Semi-Supervised learning on riemannian manifolds, Maching Learing, 56(1), 209-239(2004).

DOI: 10.1023/b:mach.0000033120.25363.1e

Google Scholar