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Pattern Recognition of Cold-Rolled Strip Plate Shape Based on IHPO-KELM
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Yaluo ZHOU1, Shaochuan ZHANG1, Wenguang LIU2, Ruicheng ZHANG1
Mining and Metallurgical Engineering | 2023, 43(6) : 162 - 168
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Mining and Metallurgical Engineering | 2023, 43(6): 162-168
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Pattern Recognition of Cold-Rolled Strip Plate Shape Based on IHPO-KELM
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Yaluo ZHOU1, Shaochuan ZHANG1, Wenguang LIU2, Ruicheng ZHANG1
Affiliations
  • 1.College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China
  • 2.Shougang Jingtang United Iron and Steel Co Ltd, Tangshan 063200, Hebei, China
Published: 2023-12-01 doi: 10.3969/j.issn.0253-6099.2023.06.035
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To address the problems of low recognition accuracy and slow speed of current plate shape pattern recognition methods, a pattern recognition model for cold-rolled strip plate shape based on IHPO-KELM was proposed. Firstly, kernel extreme learning machine (KELM) network was adopted to reduce the number of initial parameters in the network and improve the accuracy and speed of plate shape recognition. Secondly, Levy flight mechanism was added to the improved position update formula for linear combination by using the population of the predator algorithm initialized based on Sine chaos mapping, so as to improve the accuracy of the predator (HPO) algorithm, as well as to address the problem of HPO easily falling into local precocity during the iteration process. Then, the improved predator algorithm was used to optimize the regularization and kernel parameters of the KELM network model, as well as improve the accuracy of plate shape recognition. Finally, the Matlab simulation results have verified that the IHPO-KELM algorithm has the advantages of simple network structure, high convergence speed, and high recognition accuracy. The recognition accuracy of IHPO-KELM algorithm in identifying the measured data of 900HC reversible cold rolling mill of a company is higher than the KELM recognition model optimized with sparrow algorithm (SSA-KELM) by 58.8%, indicating a good generalization ability of IHPO-KEM recognition model. This provides a new idea for efficient and intelligent recognition of plate shape defects.

plate shape defects  /  cold rolled strip steel  /  plate shape recognition  /  improved predator algorithm  /  neural networks  /  kernel extreme learning machine (KELM)
Yaluo ZHOU, Shaochuan ZHANG, Wenguang LIU, Ruicheng ZHANG. Pattern Recognition of Cold-Rolled Strip Plate Shape Based on IHPO-KELM[J]. Mining and Metallurgical Engineering, 2023 , 43 (6) : 162 -168 . DOI: 10.3969/j.issn.0253-6099.2023.06.035
Year 2023 volume 43 Issue 6
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doi: 10.3969/j.issn.0253-6099.2023.06.035
  • Receive Date:2023-06-04
  • Online Date:2026-03-05
  • Published:2023-12-01
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  • Received:2023-06-04
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    1.College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, Hebei, China
    2.Shougang Jingtang United Iron and Steel Co Ltd, Tangshan 063200, Hebei, China
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表12种不同金属材料的力学参数

Family
属数
Number of
genus
种数
Number of
species
占总种数比例
Percentage of
total species (%)

Genus
种数
Number of
species
占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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